{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import explained_variance_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step = 1\n",
      "Step = 6\n",
      "Step = 12\n",
      "Step = 18\n",
      "Step = 24\n",
      "Step = 30\n",
      "Step = 36\n",
      "Step = 42\n",
      "Step = 48\n",
      "Step = 54\n",
      "Step = 60\n",
      "Step = 66\n",
      "Step = 72\n"
     ]
    }
   ],
   "source": [
    "word_len = 24\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_result_step36/'\n",
    "mae_list24 = []\n",
    "mse_list24 = []\n",
    "mape_list24 = []\n",
    "rmse_list24 = []\n",
    "evs_list24 = []\n",
    "r2_list24 = []\n",
    "for step in [1,6,12,18,24,30,36,42,48,54,60,66,72]:\n",
    "    print(f'Step = {step}')\n",
    "    file = f'{word_len}_{step}_step36_0104.csv'\n",
    "    input_path = folder_path + file\n",
    "    df = pd.read_csv(input_path)\n",
    "#     df_ = df[df['Input'] >= 1]\n",
    "    \n",
    "    mape = mean_absolute_percentage_error(df['Input'], df['Predict'])\n",
    "#     mape = format(mape, '.2%')\n",
    "    mae = mean_absolute_error(df['Input'], df['Predict'])\n",
    "    mse = mean_squared_error(df['Input'], df['Predict'])\n",
    "    rmse = mean_squared_error(df['Input'], df['Predict'], squared=False)\n",
    "    evs = explained_variance_score(df['Input'], df['Predict'])\n",
    "    r2 = r2_score(df['Input'], df['Predict'])\n",
    "    \n",
    "    mae_list24.append(mae)\n",
    "    mse_list24.append(mse)\n",
    "    mape_list24.append(mape)\n",
    "    rmse_list24.append(rmse)\n",
    "    evs_list24.append(evs)\n",
    "    r2_list24.append(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  [3.58588314336939, 3.797684538962005, 4.449432525562067, 6.3263849816278555, 7.026073301929377, 7.151189561767664, 7.519533789994243, 7.970887475746598, 8.323004522647679, 7.534074933728396, 8.122031982485677, 9.009642215962728, 9.35988445446022]\n",
      "mse:  [21.358869271926483, 25.381406226700328, 41.52551898656174, 78.32131539933569, 137.66403459370278, 138.40182188024608, 140.2894166660111, 158.81861270299999, 201.5461629900591, 166.19531767428313, 172.7708340227937, 211.14434666586405, 229.1666445413133]\n",
      "mape:  [0.10495686248207203, 0.10877589469087537, 0.12574466583188437, 0.17429861917562628, 0.1493131429650755, 0.16277502178280942, 0.1640648344189567, 0.17977796935512003, 0.18276577020232065, 0.16238158759481508, 0.18009597370243988, 0.19356237703748191, 0.20707304317267167]\n",
      "rmse:  [4.621565673224441, 5.037996251159813, 6.444029716455515, 8.849933073155734, 11.733031773318556, 11.764430367860829, 11.844383338359625, 12.602325686277116, 14.196695495433403, 12.891676294193983, 13.144231967779392, 14.530806814002588, 15.13825103971107]\n",
      "evs:  [0.9907949702458082, 0.9889984109781764, 0.9816207995726277, 0.9714920685292936, 0.9444752077130282, 0.9436677078726117, 0.9409868355548883, 0.9368868573910053, 0.9133306497173406, 0.9283158397456781, 0.9263896538015753, 0.9126848904410688, 0.9050358160698831]\n",
      "r2:  [0.990768353937492, 0.9887815993595486, 0.9815963994766094, 0.9655863416014765, 0.940207791518096, 0.9405134269221884, 0.9400105218562743, 0.9317660830351463, 0.9130396450192935, 0.9282772586902439, 0.9256428328212275, 0.909479693831083, 0.9019838926113727]\n"
     ]
    }
   ],
   "source": [
    "print('mae: ', mae_list24)\n",
    "print('mse: ', mse_list24)\n",
    "print('mape: ', mape_list24)\n",
    "print('rmse: ', rmse_list24)\n",
    "print('evs: ', evs_list24)\n",
    "print('r2: ', r2_list24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step = 1\n",
      "Step = 6\n",
      "Step = 12\n",
      "Step = 18\n",
      "Step = 24\n",
      "Step = 30\n",
      "Step = 36\n",
      "Step = 42\n",
      "Step = 48\n",
      "Step = 54\n",
      "Step = 60\n",
      "Step = 66\n",
      "Step = 72\n"
     ]
    }
   ],
   "source": [
    "word_len = 2\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_result_step36/'\n",
    "mae_list2 = []\n",
    "mse_list2 = []\n",
    "mape_list2 = []\n",
    "rmse_list2 = []\n",
    "evs_list2 = []\n",
    "r2_list2 = []\n",
    "for step in [1,6,12,18,24,30,36,42,48,54,60,66,72]:\n",
    "    print(f'Step = {step}')\n",
    "    file = f'{word_len}_{step}_step36_0104.csv'\n",
    "    input_path = folder_path + file\n",
    "    df = pd.read_csv(input_path)\n",
    "#     df_ = df[df['Input'] >= 1]\n",
    "    \n",
    "    mape = mean_absolute_percentage_error(df['Input'], df['Predict'])\n",
    "#     mape = format(mape, '.2%')\n",
    "    mae = mean_absolute_error(df['Input'], df['Predict'])\n",
    "    mse = mean_squared_error(df['Input'], df['Predict'])\n",
    "    rmse = mean_squared_error(df['Input'], df['Predict'], squared=False)\n",
    "    evs = explained_variance_score(df['Input'], df['Predict'])\n",
    "    r2 = r2_score(df['Input'], df['Predict'])\n",
    "    \n",
    "    mae_list2.append(mae)\n",
    "    mse_list2.append(mse)\n",
    "    mape_list2.append(mape)\n",
    "    rmse_list2.append(rmse)\n",
    "    evs_list2.append(evs)\n",
    "    r2_list2.append(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  [2.6284737491386223, 4.287742304568131, 4.64463851768921, 7.360201777402037, 6.782293208670582, 6.893733779794171, 7.0151522809869675, 7.832354276702414, 7.864146372208555, 8.321306419508806, 7.954758567109959, 9.268734237746132, 9.641412843173665]\n",
      "mse:  [13.113193007067462, 34.7711974361701, 44.75256592903618, 105.77448597089237, 107.61155850336614, 111.28487146284066, 119.89316918124763, 140.8293928194282, 155.4971080008052, 181.94932599127267, 171.09483998340713, 213.2647469485247, 234.18041782448768]\n",
      "mape:  [0.08509249652035758, 0.14487343090862048, 0.11792502478544557, 0.31382089572490524, 0.13930618115735213, 0.14270586228725865, 0.15703236461351544, 0.1730969584538876, 0.1660982816450563, 0.17129650179318634, 0.15991536926398728, 0.19232926826973631, 0.19285237652239257]\n",
      "rmse:  [3.621214300074971, 5.8967107302436075, 6.689735863921398, 10.284672380338247, 10.373599110403589, 10.549164491221125, 10.949573926927368, 11.867156054397709, 12.469847954197565, 13.488859328767303, 13.080322625356269, 14.603586783681765, 15.302954545593073]\n",
      "evs:  [0.9944477887567782, 0.9849503829183461, 0.9802872419369885, 0.9547159252372892, 0.953260959068207, 0.955052884467264, 0.9487718199006203, 0.9395210306834241, 0.9331957226432094, 0.9216950454312629, 0.926599140618883, 0.9087144893931366, 0.903784697312898]\n",
      "r2:  [0.9943322675442509, 0.9846313785728371, 0.9801662118654986, 0.9535236734863038, 0.9532606118941235, 0.9521685802340294, 0.9487322078663757, 0.9394948681876371, 0.9329082553117608, 0.9214785072036007, 0.9263641476755672, 0.9085706508667887, 0.8998394682274077]\n"
     ]
    }
   ],
   "source": [
    "print('mae: ', mae_list2)\n",
    "print('mse: ', mse_list2)\n",
    "print('mape: ', mape_list2)\n",
    "print('rmse: ', rmse_list2)\n",
    "print('evs: ', evs_list2)\n",
    "print('r2: ', r2_list2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step = 1\n",
      "Step = 6\n",
      "Step = 12\n",
      "Step = 18\n",
      "Step = 24\n",
      "Step = 30\n",
      "Step = 36\n",
      "Step = 42\n",
      "Step = 48\n",
      "Step = 54\n",
      "Step = 60\n",
      "Step = 66\n",
      "Step = 72\n"
     ]
    }
   ],
   "source": [
    "word_len = 6\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_result_step36/'\n",
    "mae_list6 = []\n",
    "mse_list6 = []\n",
    "mape_list6 = []\n",
    "rmse_list6 = []\n",
    "evs_list6 = []\n",
    "r2_list6 = []\n",
    "for step in [1,6,12,18,24,30,36,42,48,54,60,66,72]:\n",
    "    print(f'Step = {step}')\n",
    "    file = f'{word_len}_{step}_0.1_step36_126.csv'\n",
    "    input_path = folder_path + file\n",
    "    df = pd.read_csv(input_path)\n",
    "#     df_ = df[df['Input'] >= 1]\n",
    "    \n",
    "    mape = mean_absolute_percentage_error(df['Input'], df['Predict'])\n",
    "#     mape = format(mape, '.2%')\n",
    "    mae = mean_absolute_error(df['Input'], df['Predict'])\n",
    "    mse = mean_squared_error(df['Input'], df['Predict'])\n",
    "    rmse = mean_squared_error(df['Input'], df['Predict'], squared=False)\n",
    "    evs = explained_variance_score(df['Input'], df['Predict'])\n",
    "    r2 = r2_score(df['Input'], df['Predict'])\n",
    "    \n",
    "    mae_list6.append(mae)\n",
    "    mse_list6.append(mse)\n",
    "    mape_list6.append(mape)\n",
    "    rmse_list6.append(rmse)\n",
    "    evs_list6.append(evs)\n",
    "    r2_list6.append(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  [3.0788120454557295, 3.9370084764987303, 4.396343910815668, 5.828067764208736, 6.285422410632888, 7.289272176973325, 7.891339825501608, 8.445904783696461, 8.060859816715766, 8.127765108032667, 8.038568816585302, 8.995207219700948, 9.894202140076535]\n",
      "mse:  [15.643112623962102, 26.408083683571093, 40.685458889960195, 76.14240647372841, 102.8160159403706, 137.8725256296571, 148.19754725153123, 150.3705164404171, 155.76191858763593, 182.9556243105999, 161.2525354988328, 210.36561882927322, 235.5909963487059]\n",
      "mape:  [0.10014561967838492, 0.09736590408210802, 0.11827802164010529, 0.13396216312290196, 0.13876481872365345, 0.15316202582739816, 0.15908977515943845, 0.17089075949658855, 0.16694795830105139, 0.1842232683986122, 0.17111850253602534, 0.19917583274671188, 0.22284707421259056]\n",
      "rmse:  [3.9551374974787037, 5.138879613648396, 6.378515414260609, 8.725961636044959, 10.139823269681312, 11.74191320141897, 12.17364149511276, 12.262565654887117, 12.480461473344482, 13.526108986349323, 12.698524933976891, 14.503986308228273, 15.348973788130133]\n",
      "evs:  [0.9954426489993098, 0.9913523397736372, 0.9822612005463272, 0.9678032269589473, 0.9554534752886403, 0.943968228319175, 0.9368927482820594, 0.9377050778672218, 0.9328063246776238, 0.9222133581204193, 0.9315317005322727, 0.9121245379764628, 0.9100610688894237]\n",
      "r2:  [0.9932387956861471, 0.9883278152414889, 0.9819687038044252, 0.9665437339417995, 0.9553434803902905, 0.9407409240726068, 0.9366289080596526, 0.93539567461186, 0.9327939984968862, 0.9210442321888316, 0.9306000818488508, 0.9098135445038742, 0.8992361543534073]\n"
     ]
    }
   ],
   "source": [
    "print('mae: ', mae_list6)\n",
    "print('mse: ', mse_list6)\n",
    "print('mape: ', mape_list6)\n",
    "print('rmse: ', rmse_list6)\n",
    "print('evs: ', evs_list6)\n",
    "print('r2: ', r2_list6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step = 1\n",
      "Step = 6\n",
      "Step = 12\n",
      "Step = 18\n",
      "Step = 24\n",
      "Step = 30\n",
      "Step = 36\n",
      "Step = 42\n",
      "Step = 48\n",
      "Step = 54\n",
      "Step = 60\n",
      "Step = 66\n",
      "Step = 72\n"
     ]
    }
   ],
   "source": [
    "word_len = 12\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_result_step36/'\n",
    "mae_list12 = []\n",
    "mse_list12 = []\n",
    "mape_list12 = []\n",
    "rmse_list12 = []\n",
    "evs_list12 = []\n",
    "r2_list12 = []\n",
    "for step in [1,6,12,18,24,30,36,42,48,54,60,66,72]:\n",
    "    print(f'Step = {step}')\n",
    "    file = f'{word_len}_{step}_0.1_step36.csv'\n",
    "    input_path = folder_path + file\n",
    "    df = pd.read_csv(input_path)\n",
    "#     df_ = df[df['Input'] >= 1]\n",
    "    \n",
    "    mape = mean_absolute_percentage_error(df['Input'], df['Predict'])\n",
    "#     mape = format(mape, '.2%')\n",
    "    mae = mean_absolute_error(df['Input'], df['Predict'])\n",
    "    mse = mean_squared_error(df['Input'], df['Predict'])\n",
    "    rmse = mean_squared_error(df['Input'], df['Predict'], squared=False)\n",
    "    evs = explained_variance_score(df['Input'], df['Predict'])\n",
    "    r2 = r2_score(df['Input'], df['Predict'])\n",
    "    \n",
    "    mae_list12.append(mae)\n",
    "    mse_list12.append(mse)\n",
    "    mape_list12.append(mape)\n",
    "    rmse_list12.append(rmse)\n",
    "    evs_list12.append(evs)\n",
    "    r2_list12.append(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step = 1\n",
      "Step = 6\n",
      "Step = 12\n",
      "Step = 18\n",
      "Step = 24\n",
      "Step = 30\n",
      "Step = 36\n",
      "Step = 42\n",
      "Step = 48\n",
      "Step = 54\n",
      "Step = 60\n",
      "Step = 66\n",
      "Step = 72\n"
     ]
    }
   ],
   "source": [
    "word_len = 1\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_result_step36/'\n",
    "mae_list1 = []\n",
    "mse_list1 = []\n",
    "mape_list1 = []\n",
    "rmse_list1 = []\n",
    "evs_list1 = []\n",
    "r2_list1 = []\n",
    "for step in [1,6,12,18,24,30,36,42,48,54,60,66,72]:\n",
    "    print(f'Step = {step}')\n",
    "    file = f'{word_len}_{step}_0.1_step36.csv'\n",
    "    input_path = folder_path + file\n",
    "    df = pd.read_csv(input_path)\n",
    "#     df_ = df[df['Input'] >= 1]\n",
    "    \n",
    "    mape = mean_absolute_percentage_error(df['Input'], df['Predict'])\n",
    "#     mape = format(mape, '.2%')\n",
    "    mae = mean_absolute_error(df['Input'], df['Predict'])\n",
    "    mse = mean_squared_error(df['Input'], df['Predict'])\n",
    "    rmse = mean_squared_error(df['Input'], df['Predict'], squared=False)\n",
    "    evs = explained_variance_score(df['Input'], df['Predict'])\n",
    "    r2 = r2_score(df['Input'], df['Predict'])\n",
    "    \n",
    "    mae_list1.append(mae)\n",
    "    mse_list1.append(mse)\n",
    "    mape_list1.append(mape)\n",
    "    rmse_list1.append(rmse)\n",
    "    evs_list1.append(evs)\n",
    "    r2_list1.append(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.7645301819000396, 4.9390955813669315, 6.992309411682446, 9.337612086670516, 7.820556885516092, 8.323505852460295, 9.063156642311508, 9.896734974282957, 9.45333446169379, 9.926631605334562, 10.852727532114452, 11.153653455082063, 11.361144732239687]\n",
      "[22.600297870671916, 44.68572181191202, 85.33653811677345, 160.84675254645853, 171.56685287180463, 166.27409157536331, 212.6257454761205, 217.31796571948703, 187.22163056080745, 201.05416626668284, 323.1387661396798, 240.59938073148476, 273.9729729270139]\n",
      "[0.18416139761560535, 0.20685035043138217, 0.308365378985416, 0.2347170431765671, 0.22350414372989144, 0.19689508308190404, 0.21943661132500933, 0.29049941576449334, 0.23355791033198361, 0.3029900070791405, 0.3128178382580044, 0.33488964662748, 0.3102348075672346]\n",
      "[4.753977058282035, 6.68473797630932, 9.237777769397436, 12.682537307118736, 13.098353059518766, 12.894731155606282, 14.581692133498104, 14.741708371809796, 13.682895547390817, 14.179357047013198, 17.97606091833469, 15.51126625171152, 16.55212895451863]\n",
      "[0.9902980246051855, 0.9802758919209458, 0.9625677757514693, 0.946350788187634, 0.9259308905097201, 0.930211377273779, 0.9111982409714803, 0.908198158960251, 0.9225286620046603, 0.9136806071752515, 0.8619926182872116, 0.9104601765246396, 0.8833600867460625]\n",
      "[0.9902317885748978, 0.9802492294667884, 0.9621798933311723, 0.9293254311623498, 0.9254826355643919, 0.9285336293621701, 0.9090786189424406, 0.9066327568570034, 0.9192201964448337, 0.9132336809593202, 0.8609274337784324, 0.8968519406189391, 0.8828199261295437]\n"
     ]
    }
   ],
   "source": [
    "print(mae_list1)\n",
    "print(mse_list1)\n",
    "print(mape_list1)\n",
    "print(rmse_list1)\n",
    "print(evs_list1)\n",
    "print(r2_list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  [2.7302667347556087, 3.489665148104968, 4.646793397664251, 5.766848100740707, 6.0146649218920505, 6.83121036656868, 6.907374801880811, 7.650132967959952, 7.74316051151464, 7.966965814070402, 8.252159140472896, 8.92264995155165, 8.760864706348354]\n",
      "mse:  [12.520437987237743, 22.219661246986295, 47.38961631090467, 96.87455036796077, 107.18098150634533, 147.19865026833872, 125.05983015446895, 164.36553703169102, 152.0122571718758, 173.81080658728007, 197.9261095781998, 207.92201908661494, 187.6326729991672]\n",
      "mape:  [0.07105470654181587, 0.10745419790083896, 0.10875334928217399, 0.16755757527861737, 0.1337249360376588, 0.15517927527428083, 0.1553127660012214, 0.16306255018242846, 0.185211056095019, 0.18531893005657926, 0.19196843541467962, 0.2178099395655623, 0.20102239513878242]\n",
      "rmse:  [3.538423093305511, 4.713773567640505, 6.884011643722334, 9.842487001157826, 10.352824808058202, 12.13254508618611, 11.183015253252092, 12.820512354492351, 12.329325089877216, 13.18373265002291, 14.068621452658387, 14.419501346669895, 13.697907613908308]\n",
      "evs:  [0.9952420953623038, 0.9904659022221363, 0.9801524537365819, 0.9574457188835941, 0.9537121777789563, 0.9385605210165557, 0.9479901312543368, 0.9296472750035176, 0.9349428040605898, 0.9253316246670383, 0.9148247470652822, 0.9115478265893432, 0.9200148962949958]\n",
      "r2:  [0.994588465776244, 0.9901790681045252, 0.9789975034911693, 0.9574342225117706, 0.9534476262414029, 0.9367324566455542, 0.9465228801573085, 0.9293829343121309, 0.9344118506203625, 0.9249907416637855, 0.9148164972277173, 0.9108611472474596, 0.9197482501735278]\n"
     ]
    }
   ],
   "source": [
    "print('mae: ', mae_list12)\n",
    "print('mse: ', mse_list12)\n",
    "print('mape: ', mape_list12)\n",
    "print('rmse: ', rmse_list12)\n",
    "print('evs: ', evs_list12)\n",
    "print('r2: ', r2_list12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metric_single_plot(x, y, metric='MAE'):\n",
    "    plt.xlabel('Prediction Step')\n",
    "    plt.plot(x, y, 'b-', label=metric)\n",
    "    plt.legend()\n",
    "    if metric is 'MAPE':\n",
    "        for i in range(len(x)):\n",
    "            plt.text(x[i], y[i], format(y[i], '.2%'))\n",
    "    else:\n",
    "        for i in range(len(x)):\n",
    "            plt.text(x[i], y[i], format(y[i], '.2f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step = 1\n",
      "Step = 6\n",
      "Step = 12\n",
      "Step = 18\n",
      "Step = 24\n",
      "Step = 30\n",
      "Step = 36\n",
      "Step = 42\n",
      "Step = 48\n",
      "Step = 54\n",
      "Step = 60\n",
      "Step = 66\n",
      "Step = 72\n"
     ]
    }
   ],
   "source": [
    "# lstm\n",
    "word_len = 1\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_result_step36/'\n",
    "mae_list = []\n",
    "mse_list = []\n",
    "mape_list = []\n",
    "rmse_list = []\n",
    "evs_list = []\n",
    "r2_list = []\n",
    "for step in [1,6,12,18,24,30,36,42,48,54,60,66,72]:\n",
    "    print(f'Step = {step}')\n",
    "    file = f'lstm_{step}_0.1_step36.csv'\n",
    "    input_path = folder_path + file\n",
    "    df = pd.read_csv(input_path)\n",
    "#     df_ = df[df['Input'] >= 1]\n",
    "    \n",
    "    mape = mean_absolute_percentage_error(df['Input'], df['Predict'])\n",
    "#     mape = format(mape, '.2%')\n",
    "    mae = mean_absolute_error(df['Input'], df['Predict'])\n",
    "    mse = mean_squared_error(df['Input'], df['Predict'])\n",
    "    rmse = mean_squared_error(df['Input'], df['Predict'], squared=False)\n",
    "    evs = explained_variance_score(df['Input'], df['Predict'])\n",
    "    r2 = r2_score(df['Input'], df['Predict'])\n",
    "    \n",
    "    mae_list.append(mae)\n",
    "    mse_list.append(mse)\n",
    "    mape_list.append(mape)\n",
    "    rmse_list.append(rmse)\n",
    "    evs_list.append(evs)\n",
    "    r2_list.append(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lstm mae:  [1.7132472251502402, 3.7673329138701925, 5.990289844271501, 7.985848860628577, 9.955803914174036, 11.56989943653257, 12.441935966693466, 15.310825925217944, 14.842333667706479, 16.661119250701756, 18.25978533615327, 20.988736039517523, 23.078373592044834]\n",
      "lstm mse:  [6.886840572653653, 32.05722720748212, 99.91854391312486, 165.31732477867786, 239.88303659893495, 334.50547107071515, 384.25784619856586, 461.44553906427313, 444.3580562113864, 573.8147209881595, 633.3828916815395, 751.3129692131419, 862.0989816577079]\n",
      "lstm mape:  [0.04406901008594766, 0.11735641095061301, 0.12182542573174857, 0.1973448075706543, 0.2602374951273728, 0.2695442261846815, 0.28128446055209455, 0.4541254148282097, 0.4172220023775058, 0.4115869659485251, 0.6042528801882583, 0.8051268610175362, 0.8425427196520054]\n",
      "lstm rmse:  [2.6242790576944466, 5.661910208355668, 9.995926365931517, 12.857578495917412, 15.488157947249084, 18.28949072748378, 19.602495917575542, 21.48128345942749, 21.07980209137141, 23.954430091074165, 25.16709938951129, 27.410088821693773, 29.361522127739015]\n",
      "lstm evs:  [0.9970677662891337, 0.9860593472137187, 0.9557229575627904, 0.9278763039390626, 0.8976771914127744, 0.866630536671364, 0.850084325987438, 0.8273159819276952, 0.8163296380662607, 0.778319470701098, 0.7366924234914158, 0.6965115531731225, 0.7120050419934936]\n",
      "lstm r2:  [0.997023396985756, 0.9858309340694766, 0.9557173272740771, 0.927361103254154, 0.8958105755223025, 0.8562259955870134, 0.8356866240240857, 0.8017471878110731, 0.8082745226532304, 0.7523662798140793, 0.7274044671294164, 0.6779024346340841, 0.6312744966226755]\n"
     ]
    }
   ],
   "source": [
    "print('lstm mae: ', mae_list)\n",
    "print('lstm mse: ', mse_list)\n",
    "print('lstm mape: ', mape_list)\n",
    "print('lstm rmse: ', rmse_list)\n",
    "print('lstm evs: ', evs_list)\n",
    "print('lstm r2: ', r2_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step = 1\n",
      "Step = 6\n",
      "Step = 12\n",
      "Step = 18\n",
      "Step = 24\n",
      "Step = 30\n",
      "Step = 36\n",
      "Step = 42\n",
      "Step = 48\n",
      "Step = 54\n",
      "Step = 60\n",
      "Step = 66\n",
      "Step = 72\n"
     ]
    }
   ],
   "source": [
    "# transformer 126\n",
    "word_len = 1\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_result_step36/'\n",
    "mae_transformer_list = []\n",
    "mse_transformer_list = []\n",
    "mape_transformer_list = []\n",
    "rmse_transformer_list = []\n",
    "evs_transformer_list = []\n",
    "r2_transformer_list = []\n",
    "# for step in [1,6,12,18,24,30,36,42,48,54,60,66,72]:\n",
    "for step in [1,6,12,18,24,30,36,42,48,54,60,66,72]:\n",
    "    print(f'Step = {step}')\n",
    "    file = f'transformer_{step}_step36_1231.csv'\n",
    "    input_path = folder_path + file\n",
    "    df = pd.read_csv(input_path)\n",
    "#     df_ = df[df['Input'] >= 1]\n",
    "    \n",
    "    mape = mean_absolute_percentage_error(df['Input'], df['Predict'])\n",
    "#     mape = format(mape, '.2%')\n",
    "    mae = mean_absolute_error(df['Input'], df['Predict'])\n",
    "    mse = mean_squared_error(df['Input'], df['Predict'])\n",
    "    rmse = mean_squared_error(df['Input'], df['Predict'], squared=False)\n",
    "    evs = explained_variance_score(df['Input'], df['Predict'])\n",
    "    r2 = r2_score(df['Input'], df['Predict'])\n",
    "    \n",
    "#     mape = mean_absolute_percentage_error(df_['Input'], df_['Predict'])\n",
    "# #     mape = format(mape, '.2%')\n",
    "#     mae = mean_absolute_error(df_['Input'], df_['Predict'])\n",
    "#     mse = mean_squared_error(df_['Input'], df_['Predict'])\n",
    "#     rmse = mean_squared_error(df_['Input'], df_['Predict'], squared=False)\n",
    "#     evs = explained_variance_score(df_['Input'], df_['Predict'])\n",
    "#     r2 = r2_score(df_['Input'], df_['Predict'])\n",
    "    \n",
    "    mae_transformer_list.append(mae)\n",
    "    mse_transformer_list.append(mse)\n",
    "    mape_transformer_list.append(mape)\n",
    "    rmse_transformer_list.append(rmse)\n",
    "    evs_transformer_list.append(evs)\n",
    "    r2_transformer_list.append(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transformer mae:  [4.604833378415965, 7.385853339474861, 5.757082965183376, 7.1847478290692175, 8.832210289181104, 10.05261489104151, 10.682002406654476, 9.029757942268313, 10.352100243905424, 11.511640891548808, 12.09504898648779, 14.681997462289415, 16.07159464457278]\n",
      "transformer mse:  [34.74179567424824, 87.04423942263976, 72.1801815799185, 133.29198911223799, 176.199526235311, 226.58372036603342, 284.30704738770066, 191.1709307531927, 229.2635047477694, 253.2652464071832, 313.6164409568098, 367.50176126340324, 451.4144453508215]\n",
      "transformer mape:  [0.2638162634447613, 0.3007390950094829, 0.1504084136941973, 0.17241684060418655, 0.20830965497479162, 0.2738697578413001, 0.2726788189715106, 0.22041847029622352, 0.24023253104433098, 0.3129722970781933, 0.2945565429422835, 0.35003602183918575, 0.4041237151738387]\n",
      "transformer rmse:  [5.8942171383694575, 9.329750233668625, 8.495892041446766, 11.545214987701094, 13.274016959282182, 15.052698109177419, 16.861407040567542, 13.826457635750117, 15.141449889220299, 15.914309485717036, 17.70921909505921, 19.170335449944616, 21.246516075602173]\n",
      "transformer evs:  [0.9887317912400961, 0.962502906995622, 0.9689834357362013, 0.9414894958808653, 0.9239023240503546, 0.9032293108716697, 0.8784270905859075, 0.9186222039041223, 0.9040841403436551, 0.8990866593569385, 0.8650459455938981, 0.8424907060878587, 0.8077453587706143]\n",
      "transformer r2:  [0.9849840383796823, 0.9615270666028226, 0.9680106291282597, 0.9414327382376014, 0.9234705067436956, 0.902611910329771, 0.8784268135779925, 0.9178664188466321, 0.9010805491843064, 0.8907016274284312, 0.8650256551568155, 0.8424472524484704, 0.806927020985782]\n"
     ]
    }
   ],
   "source": [
    "print('transformer mae: ', mae_transformer_list)\n",
    "print('transformer mse: ', mse_transformer_list)\n",
    "print('transformer mape: ', mape_transformer_list)\n",
    "print('transformer rmse: ', rmse_transformer_list)\n",
    "print('transformer evs: ', evs_transformer_list)\n",
    "print('transformer r2: ', r2_transformer_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step = 6\n",
      "Step = 30\n",
      "Step = 36\n",
      "Step = 48\n",
      "Step = 54\n",
      "Step = 66\n",
      "Step = 72\n"
     ]
    }
   ],
   "source": [
    "# transformer 0103\n",
    "word_len = 1\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_result_step36/'\n",
    "mae_transformer_list1 = []\n",
    "mse_transformer_list1 = []\n",
    "mape_transformer_list1 = []\n",
    "rmse_transformer_list1 = []\n",
    "evs_transformer_list1 = []\n",
    "r2_transformer_list1 = []\n",
    "# for step in [1,6,12,18,24,30,36,42,48,54,60,66,72]:\n",
    "for step in [6,30,36,48,54,66,72]:\n",
    "    print(f'Step = {step}')\n",
    "    file = f'transformer_{step}_step36_0103.csv'\n",
    "    input_path = folder_path + file\n",
    "    df = pd.read_csv(input_path)\n",
    "#     df_ = df[df['Input'] >= 1]\n",
    "    \n",
    "    mape = mean_absolute_percentage_error(df['Input'], df['Predict'])\n",
    "#     mape = format(mape, '.2%')\n",
    "    mae = mean_absolute_error(df['Input'], df['Predict'])\n",
    "    mse = mean_squared_error(df['Input'], df['Predict'])\n",
    "    rmse = mean_squared_error(df['Input'], df['Predict'], squared=False)\n",
    "    evs = explained_variance_score(df['Input'], df['Predict'])\n",
    "    r2 = r2_score(df['Input'], df['Predict'])\n",
    "    \n",
    "#     mape = mean_absolute_percentage_error(df_['Input'], df_['Predict'])\n",
    "# #     mape = format(mape, '.2%')\n",
    "#     mae = mean_absolute_error(df_['Input'], df_['Predict'])\n",
    "#     mse = mean_squared_error(df_['Input'], df_['Predict'])\n",
    "#     rmse = mean_squared_error(df_['Input'], df_['Predict'], squared=False)\n",
    "#     evs = explained_variance_score(df_['Input'], df_['Predict'])\n",
    "#     r2 = r2_score(df_['Input'], df_['Predict'])\n",
    "    \n",
    "    mae_transformer_list1.append(mae)\n",
    "    mse_transformer_list1.append(mse)\n",
    "    mape_transformer_list1.append(mape)\n",
    "    rmse_transformer_list1.append(rmse)\n",
    "    evs_transformer_list1.append(evs)\n",
    "    r2_transformer_list1.append(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transformer mae:  [11.032362688031148, 16.145402459782392, 15.927722315428776, 16.945763692166807, 13.889226354377458, 13.030605450000678, 15.935717487566976]\n",
      "transformer mse:  [186.48959376969646, 442.4827874213165, 431.46405259605785, 506.8873813942518, 361.88213893726663, 313.36214683590106, 412.8089108373921]\n",
      "transformer mape:  [0.36101473629953956, 0.3752444964027118, 0.3807585072467853, 0.3615615884686923, 0.32577973793804765, 0.34711824955587367, 0.42246324416229986]\n",
      "transformer rmse:  [13.656119279271708, 21.03527483588737, 20.771712798805442, 22.514159575570478, 19.023200018326744, 17.702037928891155, 20.317699447461862]\n",
      "transformer evs:  [0.9456493913161623, 0.8647095988977174, 0.8240000586733811, 0.7813011613292198, 0.8748436427771599, 0.9008374313488324, 0.875210933778304]\n",
      "transformer r2:  [0.9175729288008222, 0.8098161981394496, 0.8155006701992691, 0.781295232976184, 0.8438272545891643, 0.8656576038087179, 0.8234388664344834]\n"
     ]
    }
   ],
   "source": [
    "print('transformer mae: ', mae_transformer_list1)\n",
    "print('transformer mse: ', mse_transformer_list1)\n",
    "print('transformer mape: ', mape_transformer_list1)\n",
    "print('transformer rmse: ', rmse_transformer_list1)\n",
    "print('transformer evs: ', evs_transformer_list1)\n",
    "print('transformer r2: ', r2_transformer_list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metric_four_plot(x1, x2, x3, x4, y1, y2, y3, y4, metric='MAE'):\n",
    "    plt.xlabel('Prediction Step')\n",
    "    plt.plot(x1, y1, 'b-', label=metric+'(LSTM)')\n",
    "    plt.plot(x2, y2, 'g-.', label=metric+'(K=1)')\n",
    "    plt.plot(x3, y3, 'r--', label=metric+'(K=12)')\n",
    "    plt.plot(x4, y4, label=metric+'(Transformer)')\n",
    "    plt.legend()\n",
    "    if metric is 'MAPE':\n",
    "        for i in range(len(x1)):\n",
    "            plt.text(x1[i], y1[i], format(y1[i], '.2%'))\n",
    "        for i in range(len(x2)):\n",
    "            plt.text(x2[i], y2[i], format(y2[i], '.2%'))\n",
    "        for i in range(len(x3)):\n",
    "            plt.text(x3[i], y3[i], format(y3[i], '.2%'))\n",
    "        for i in range(len(x4)):\n",
    "            plt.text(x4[i], y4[i], format(y4[i], '.2%'))\n",
    "    else:\n",
    "        for i in range(len(x1)):\n",
    "            plt.text(x1[i], y1[i], format(y1[i], '.2f'))\n",
    "        for i in range(len(x2)):\n",
    "            plt.text(x2[i], y2[i], format(y2[i], '.2f'))\n",
    "        for i in range(len(x3)):\n",
    "            plt.text(x3[i], y3[i], format(y3[i], '.2f'))\n",
    "        for i in range(len(x4)):\n",
    "            plt.text(x4[i], y4[i], format(y4[i], '.2f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metric_five_plot(x1, x2, x3, x4, x5, y1, y2, y3, y4, y5, metric='MAE'):\n",
    "    plt.xlabel('Prediction Step')\n",
    "    plt.plot(x1, y1, 'b-', label=metric+'(LSTM)')\n",
    "    plt.plot(x2, y2, 'g-.', label=metric+'(K=1)')\n",
    "    plt.plot(x3, y3, 'r--', label=metric+'(K=12)')\n",
    "    plt.plot(x4, y4, label=metric+'(Transformer)')\n",
    "    plt.plot(x5, y5, label=metric+'(K=6)')\n",
    "    plt.legend()\n",
    "    if metric is 'MAPE':\n",
    "        for i in range(len(x1)):\n",
    "            plt.text(x1[i], y1[i], format(y1[i], '.2%'))\n",
    "        for i in range(len(x2)):\n",
    "            plt.text(x2[i], y2[i], format(y2[i], '.2%'))\n",
    "        for i in range(len(x3)):\n",
    "            plt.text(x3[i], y3[i], format(y3[i], '.2%'))\n",
    "        for i in range(len(x4)):\n",
    "            plt.text(x4[i], y4[i], format(y4[i], '.2%'))\n",
    "        for i in range(len(x5)):\n",
    "            plt.text(x5[i], y5[i], format(y5[i], '.2%'))\n",
    "    else:\n",
    "        for i in range(len(x1)):\n",
    "            plt.text(x1[i], y1[i], format(y1[i], '.2f'))\n",
    "        for i in range(len(x2)):\n",
    "            plt.text(x2[i], y2[i], format(y2[i], '.2f'))\n",
    "        for i in range(len(x3)):\n",
    "            plt.text(x3[i], y3[i], format(y3[i], '.2f'))\n",
    "        for i in range(len(x4)):\n",
    "            plt.text(x4[i], y4[i], format(y4[i], '.2f'))\n",
    "        for i in range(len(x4)):\n",
    "            plt.text(x4[i], y4[i], format(y4[i], '.2f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = x2 = x3 = x4 = x5 = [1,6,12,18,24,30,36,42,48,54,60,66,72]\n",
    "metric_five_plot(x1,x2,x3,x4,x5,mae_list,mae_list1,mae_list12,mae_transformer_list,mae_list6,metric='MAE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric_five_plot(x1,x2,x3,x4,x5,mape_list,mape_list1,mape_list12,mape_transformer_list,mape_list6,metric='MAPE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric_five_plot(x1,x2,x3,x4,x5,rmse_list,rmse_list1,rmse_list12,rmse_transformer_list,rmse_list6,metric='RMSE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric_five_plot(x1,x2,x3,x4,x5,r2_list,r2_list1,r2_list12,r2_transformer_list,r2_list6,metric='R^2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metric_double_plot(x1, x2, y1, y2, metric='MAE'):\n",
    "    plt.xlabel('Prediction Step')\n",
    "    plt.plot(x1, y1, 'b-', label=metric+'(LSTM)')\n",
    "    plt.plot(x2, y2, 'r--', label=metric+'(K=12)')\n",
    "    plt.legend()\n",
    "    if metric is 'MAPE':\n",
    "        for i in range(len(x1)):\n",
    "            plt.text(x1[i], y1[i], format(y1[i], '.2%'))\n",
    "        for i in range(len(x2)):\n",
    "            plt.text(x2[i], y2[i], format(y2[i], '.2%'))\n",
    "    else:\n",
    "        for i in range(len(x1)):\n",
    "            plt.text(x1[i], y1[i], format(y1[i], '.2f'))\n",
    "        for i in range(len(x2)):\n",
    "            plt.text(x2[i], y2[i], format(y2[i], '.2f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metric_triple_plot(x1, x2, x3, y1, y2, y3, metric='MAE'):\n",
    "    plt.xlabel('Prediction Step')\n",
    "    plt.plot(x1, y1, 'b-', label=metric+'(LSTM)')\n",
    "    plt.plot(x2, y2, 'g-.', label=metric+'(K=1)')\n",
    "    plt.plot(x3, y3, 'r--', label=metric+'(K=12)')\n",
    "    plt.legend()\n",
    "    if metric is 'MAPE':\n",
    "        for i in range(len(x1)):\n",
    "            plt.text(x1[i], y1[i], format(y1[i], '.2%'))\n",
    "        for i in range(len(x2)):\n",
    "            plt.text(x2[i], y2[i], format(y2[i], '.2%'))\n",
    "        for i in range(len(x3)):\n",
    "            plt.text(x3[i], y3[i], format(y3[i], '.2%'))\n",
    "    else:\n",
    "        for i in range(len(x1)):\n",
    "            plt.text(x1[i], y1[i], format(y1[i], '.2f'))\n",
    "        for i in range(len(x2)):\n",
    "            plt.text(x2[i], y2[i], format(y2[i], '.2f'))\n",
    "        for i in range(len(x3)):\n",
    "            plt.text(x3[i], y3[i], format(y3[i], '.2f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x2 = [1,6,12,18,24,30,36,42,48,54,60,66,72]\n",
    "x1 = x3 = [1,6,12,18,24,30,36,42,48,54,60,66,72]\n",
    "metric_triple_plot(x1,x2,x3,mae_list,mae_list1,mae_list12,metric='MAE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric_triple_plot(x1,x2,x3,rmse_list,rmse_list1,rmse_list12,metric='RMSE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric_triple_plot(x1,x2,x3,mape_list,mape_list1,mape_list12,metric='MAPE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0jhSfNpLNw3zkyBGefvppgAw/bWiaZv10EMhCDRvCvj12fNbnWcK/X8CzT3jx44+w+NgfjN44GltljFQ+FXGKuPg4AG7evImDg4P5CzqlmdFOnjzJd5MnExISwpKFC7mvFM5371L0o4+wEeGzkiXh9m2ef/75h+7wIeXUFMnnYU5ITQEkSU1x6tQp3Nzc6Nu3L7Vr16Z///7cNTVYpyQz1U6VKlWiZs2a+Pv76/EOmpZFdBDIYg4O8OGHcPAg1KkDr70Guz6dwILm+7G3tSde4mn2czMqTazE19u+pnDhwkRHR3P69Gmio6M5evToQzOYVatWjQULFgDw0ciRxIlwc8EC7n79NU42NnR+5x1q+/kxePBgqlSp8lCZUuoMkFJqioiICPz9/fnuu+/MqSliY2PZu3cvr7/+Ovv27cPZ2TnFL3fIfLUTwD///ENISAi5raOApuUWOghkkypVYN06mDULjhyBFk+UZuRIiIwUJrSaQHXX6ry3+j1GbxqNf11/Wrdujbe3Nw0aNMDV1ZVp06aZp8h89tlnzfME/Prrr8bMaA4OxDZpwj3gWMWKxBUuzM2bN7Hbswf69oVEd9jpSU0REhLCnDlzCA8Px8PDA3d3d9zd3WnQoAEAXbt2Ze/evSlec2aqnTRNyx46CGQjpaBPHzh6FLp3h1GjoE5tW0pe7crqXqv5uePPnJWzbA3bygfBH3DixAnq1KlD2bJlCQoKIigoCIAWLVpw69YtIiMjOXPmDBEREeYv6PLly3M0NJQDBw4we/Zsjl66BPPnQ82a8MwzsHYt9Xx9zakpoqOjCQ4Opn379knKeuPGDaKjjR5NiVNTlC5dmvLly/Pvv/8CsG7duiRprhPLTLWT8XkpWrVqRUBAAD/++GPmfwGZpKu2tDwpI8OMs/plTWkjstLKlSKVKomAyKuvily/LnLy6klxdHMUBiNt57QVnxo+cujQoST7RURESFRUlIiI/Pjjj9K7d2/zusaNG8uxY8dERGTEiBEyZMgQkatXRT79VKRUKeNk77wjy5cvlypeXuLp4CCfVqki8tJLMrVFC5naqZPIrl0PUlN4ekqn555Lkppi3759EhAQIDVr1pQOHTokWZfY/Pnz5ZVXXjG/nzNnjgwaNCjJNjdv3pQ+ffpIrVq1pFevXlK3bl0JCQkREZHz58+LiMjly5fFz89PNmzYkNGPOtNiY2PF09NTTp48KVFRUeLn5yeHDx9Oss2QIUNk5MiRIiJy9OhRad68uXldxYoVJTw8PFvLrOUvZDBthGUbQRvgX4x5hIelsL4YsBg4AOwEaiRaFwocBEIsLWR+CQIiInfuiLz/voitrYibm8jMmSJLly4Tt/JuQjGkYOuC8td/f1mcO+iRX9D374v8/rvItm3G+2vXRJ55RiQgQKRCBRFHR+NPYvx4Y/3x48Z7EClYUKRiRZF69USWLDHWX7wo8tVXIrNniyxfLrJrl8iZMyKmALV161Zp1aqV+fSff/65fP7556l+FvHx8VKxYkW5efPmQ+tGjBgh48aNy8An/HhYci3PPvusbNq0yfze09NTLl26JCI6CGhZL8uCAMYk8ScBT8AB2A/4JNtmHDDC9HN1YF2idaGAa3oKlZ+CQIJ9+0SefNL4jTzxhMju3SKHrxyWuj/WlQ2h2XQHHB8vcvu28RIxHk1mzhQZO1bk3XdFevcWad3aeIQREVm//kGQSPz63/9ERCTm7FnxKF1aTh0/br57tvSp5s6dO3Lr1i3zzw0bNpQVK1Zkw4eQsgULFjz0VDNw4MAk2wwfPlzeeecdERHZsWOH2Nrayu7du0VEpFKlSlK7dm2pU6eO/PDDD9lXcC3fyGgQsCSVdH3ghIicAlBKBQMdgMTdPHyAL0zVS8eUUpWUUqVERLfwWcjf3xgU/MsvMHQo1KsHAwb4sOLTnbi6Gj13vtryFfXL1SewUmDWFEIpKFTowftixaBfv9S3b9oUbt6E8HC4cuXBv/XqAWA3dy6TL12idfXqxBUqRL+ePfH18TE3cKeafhu4fPkynTp1AowpM1944QXatGmTNddtAeP/WFKWTvYDsGXLFsqWLcuVK1do2bIl1atXp2nTptlSdk17pLSiBNAVmJHofW9gcrJtPge+Nv1cH4gFAkzvTwN7gT3AAEsiU358Ekjsxg2Rt982qoiKFxeZNk3k9v17Uu27ahK0LCini2e5e/dE5s8X6dRJxMHBeErw9jZXF+UmealqS8ubyOCTgCW9g1LKY5z8tmgsUEwpFQK8CewzBQKARiJSB3gGGKiUSvH2Ryk1QCm1Wym1Ozw83IJi5V1Fi8I330BIiNGpJygIAhs7MdV/D+NbjQdg38V97L2YctdMq+HkBN26waJFRprsmTOhUydj8ATAG2/A+PFg6g2UGZnpuQPGmIbatWvTrl07kq2Af/+lXrVqGe5RdffuXW7fvg3A3bt3Wb16NTVq1Mj0NWvaY5FWlAAaAqsSvR8ODH/E9gqjHaBICutGAkPSOmd+fxJILD5eZN48kbJljRvpfv1ELl8Waf5zc7EbbSdjNoyRmLiYnC5m+kVFGY0fIKKUyFNPifzwg9FYnU6Z7bkjIjJhwgTp2bOntG3bVmTtWpH33hNp2lSkUCGjjGXLyvJly6RKlSrpnuzn5MmT4ufnJ35+fuLj42PeN6etWLFCqlatKpUrV5YvvvjiofXXr1+Xjh07Ss2aNaVevXpy8OBBERG5f/++1KtXz3w9n3zySXYXXUsBWdgwbAecAjx40DDsm2wbF8DB9POrwBzTz85A4UQ/bwXapHVOHQQeduuWyNChInZ2Ii4uImMnXpPn5/cQRiJPzHhC/rv6X04XMWOOHxcZPVqkWjXjz/Hbb43l9+8bXacskOGeO/v2iSxeLOfefFOalygh61avNoLAoEHGLG8NGhg/z5wpEhxs7BgfbwSvgQONXlYZmlIu52UmcMbHx8ttU+eB6OhoqV+/vmxL6HGm5ZiMBoE0q4NEJBYYBKwCjgLzReSwUipIKRVk2swbOKyUOoZR7ZMw9r8UsFkptR+j6+hyEVlp+XOKlqBwYfjySyP9RN26MGxwcY5+Oo8RvvP49+q/+P/gz/e7vk+xAdOqeXnBxx8bI+j27oUXXzSW//67MclOr17w118QE5PqISwalFalCovmzwdg52efcebUKcJq14ZOnXj7u+/4qnhxbG7cMDYeMwZu3YLt2+G774zG8eefN9bdvg0VKxpVWw0bGkPBR46EM2ce1yeSLTIzmlspRSFTB4KYmBhiYmIeaiTXcpGMRI6sfukngUeLjzd6YZYvb9w8d345TAJntBZGIq1/aS1hN8PSPIbVVwXs2ycyYIBIsWLGRZYoIRIUlGKj8vz586V169bm6+nWubMM6thR5MsvRbp2FalUSUJBKri5iaOjo5QoUkR8ixaVkA8+kIWffy5ubm7i5+cnFStWlCpVqlhWvps3RWbNEnn6aaM6a+FCY/nVqyJXrmTq0rPjd5PZLq+xsbFSq1YtcXZ2lqFDh2bqerXHg6wcLJbdLx0ELHPnjshHHxkdbwoVjpfOX0wRp0+dpNjYYrLwyMJU94uNjZXSpUtLpUqVxNPTU0qXLv1QVcCgQYOkevXqUrNmTalRo4Y0aNBARETu3bsnderUET8/P/H29payZctmbVVAVJTI0qUiPXoYAykS/PabSEiIyN27sun778XJ3l5O/v67REVFSekSJeTthDELHh4i3bvLkKZNZeTgwSIicuTIEXF0dJSbN2/KBx98IGXLlpWKFStKqVKlxMbGRlq3bp2+MoaFiURGGj+PHm3U2bVrZ1Qh3buXrkNlVzVNZkdzJ4iIiJDAwEBzINJyjg4C+dh//xkDf0Gkcv1/xXtCA/n90O+pbr9p0yZxcnIyf9GULl1a3n777STbeHh4SN++fUXE+KJxdHSUS5cuJfmiuXHjhhQsWFBmzJiR6rke611tQv17VJRI0aKS0Ki8EcQJ5NTrrxvXU6qUDO7QQSTRCN2WLVvK33//LSLGoLRChQqZR/MmLmuRIkVk+/btqV5Pmo4cMRpvypUzyle4sPFEY2HbQWZHJie4e/eu1K5dO9VreZxdXkeOHPnILq8Z/Rs4e/asBAYGSvXq1cXHx0e+TWgv0lKkg0A+Fx9vZHPw8BBBxUr37iLnzonM3DtTVp1YlWTbTz/9VMqVK2d+361bN3niiSeSbFO5cmXp3r27iBhVAYCsWmUcJ3FVQMmSJVP9osnSu9rwcJGpU0U++UQWDB0qrZ56ytxzp2vXrjJw4MAkPXd69+4tLi4uUq1aNQkMDBQbG5uHqjYcHR3F09PT8g/9UWJjRf7+W6RvX5FevR4snzxZZP/+VHd7rNU0b70lEhr6YMfvvxd57TWRPn0kZtw48ShTRk7t35/u0dxXrlyRiIgIETGeDBs3bizLli1L5WPI+N/AhQsXZM+ePSIicuvWLalSpcpD+2oPZDQIWDJiWMsFlIL27aFlS/jqK1vGjoVly+MoNnQKtSuXo1XlVuZtr1+/jrOzs/l98eLFOXbsWJLjdejQgZUrV+Lv70+ZMmUAuHbtWpJtRIQCBQokOVZiiRsfAXPjY+Kso0eOHGH48OFA0sbHUqVKPbrx0dXVGEAByIIFlL92jVXr1wPwyy+/sHPnTnPWVYDJkydja2vLvn37cHd3p06dOubRvLa2toSEhHDjxg06derEoUOHMt+P39YWmjUzXgnCw+Gdd4xGbj8/o9G7Z09wdzdvYvxfTuqhkcnvvcfg11/Hv2pVapYv/2Bk8mefYbtmDSF373IjLo5OkyZxaNkyapw6ZewYHGzkMbezw272bCYDrZ94grgyZejXrx++u3czbcYMqFCBoHfeSXU098WLF3n55ZeJi4sjPj6e7t27Pzy+wiQzfwNlypQx/+0VLlwYb29vzp8/n2rWWi1jdCrpPMbJCUaMMP6vt25py4Uxmzk2djarV8PpiNPsCNtB8eLFk8wGljwoAIwYMYL69esDUKJECRwcHKhQoQLw4Evz/Pnz2NraMnv27BTLktlU0nFxcfj7+1OyZElatmxpnsMguczMj5CYi4sLgYGBrFyZRR3Y3NzgwgWYPBkKFjTyg1SoAAsXGutFcHdx4dzRo7BmDcyeTdhvv1HW9EXIxx9DmTIUcXNj1oIFhBw/zpydOx9cy/XrxuC2gABc3niDwFatWJk4NcXffxuB6OJFuHQJm1GjUCVKoJTCFuDVVwn69luC3n0XqlSh+ldfUcPVFQcHB8LCwsy/u2LFiuHi4kJMTAwiQtGiRVO95Mz+DSQIDQ1l3759qf4N5DUZHfyolCqvlPpHKXVUKXVYKTX4oZ2T0U8CeZSHhzEv/cqVTrz5phOtW4P74A+5UHw+pa+X5uKVi7SY2AL3cu78tfYvareozc8hP1OmcBlaVW5FfHw8333/Hc6OzkyfPp0//viDmjVrEh4ejr29PS4uLhQoUID4+HiuX7+eYhksuqt9RL6dxHfoHTp24Pe/f8e1kisRkRHciLzBjcgbuBZ0pVOtTubRvOXKlSM4OJi5c+cmOc+NGzcoWLAgDg4OSUbzJr6e+/fvs3btWj744IPH9FtIgasrDBxovE6cgF9/NXIwAUyfTr3XXuM4cLpVK8oBwcDcUaOM9Z6e0LYtN0qUoGDFijhUrMiMHTtoGhpqXMuwYUmvpVUrPuja9cG5bW3NP8a5ujLw559Zs2ED7u7u1KtXj/Z//43P3btGV909e/h8zRr8GzRg8bZtHNu4kYH16rGubVvsqlRhQteu1Hn+eW4XKEBAQAAtW7ZM8Q49s38DAHfu3KFLly58++23FClSJKOffK6RMCPfmjVrHvxu2rdP8vkmzMi3ePFijh07xsCBAxNWxQLvichepVRhYI9Sao2IPDzPrIkOAnlcmzbGpGJffw1jvpqGqueFW6PjhDstZuOYjcRLPHHRcWx228zm/9tM8YLFuTb/Gjt37qRj745gA9XLVKdt27Z8sPEDju89TsisEOxs7LARG2JiYqjauCpHw4/i5uxGMcdi2NoYXzaJ79DjJZ7dx3YT7RTNsn+Xmb/II+5H4PK8C7U71ub6veucWncKDw8P7sfcp/SE0vxfk//j/Ubv4/+EPz3G9oBGD1/j4AKDafpyU5q1aIYttkbVhq+vRYnq0lO18dh5eRljDBIUKoTdV18xOTyc1vPmEaeUcS0NGjy4lhkzOLptW9ZU02zahM/w4dC6NQBH2rZluCkgVi9XjlBbWy7v2kWZhQspAzBoEIXnzzeqaQ4cwOfMGQgIgJIlzedJz1MaGEHDw8PD/JQWExNDly5dePHFF+ncuXPGPudcJqNVaICdiFwELgKIyG2l1FGgHEkTfiaVkYaErH7phuGscfasSMeORneAqlWXS8WKRkPqmDFj5GbkTRk9frT835f/JyJG75FS5UtJ6UqlzSkQnpv7nJQbWk5sytgIJRHcEAIRRj54Nf/5QToG70neUqRUETl16pTcu39PKIXwRtLt+QApNLqQlP+6vJR7sZzUbl1bRIyJZF7/3+uy9uRauXfvnjzZ6En5v6n/JxtCN8iBSwfk7I2zcivylmw+s1l6LeolDmMchJFkX9rtXC5TDdDXr4usWycybpyc3rxZypcvLzcnTRJzKnF3d5H27UXGjJGYixeldOnS4uHhYe6OnLwB+vTp09K+fXupWbOmVKpUSZ577jkRMToHVK5cWZycnMTX1/eR15PRHkgiIn379hU3N7c0z5FdMvq7AY5I4l4/UAk4SwopfJJs96iVOfXSQSDrJOQiKlHCGF/wxRciMRlIPRQZEylhN8Mk5GKIrDm5RuYdnCeTtk9K0jX1k78/kfenvG/utfPCWy/ImpNrZNgXw2T0+NFy9e5V2bh5Y4r5dvbv3y/+/v5Ss2ZN8fX1lVGjRj2yPOF3w2XS9knmPEoTtk6Q4WuHS3wuTeuQ1R7HOIHbt29LnTp1ZOHChUZek/XrRSZMEHnhBSMNiFISe/myMSalRAnxKFRIShUpIoeDg2Xqd9+Ze2698MILUrx4calWrZq0aNFCmjRpIiJGV2ZAvLy8xNHRUWrVqiXLly9/6Foymztqw4YNsmfPHqsJAhn93QCH5UEAKISRubmzpPF9m+Nf+Cm9dBDIepcuiXTpYvwF1K0rktfG+ry27DXpMK+D+f3Ws1slNi425wpkZTI7TiA6OlpatWolEyZMSP0kt249OM9nn4mUKiWfg3wORm6mZs1E4uONcQ9//WUeS5F83MPp06cf+QX9OMZWpHWOBNnxxJHR3w2wV4wAYI+R5uddseD7VvcOyqdKlYL//c+Ygz40FOrUgc8+e2SKnlxlWrtpLOxu9Lo5HXGaRj81wmOiB59u/JSLty9m6JiZSVfdr18/SpYsmaMppO9G32XliZVM3jmZi4UvcvjYYfYf25/u1NgiwiuvvIK3tzfvvvtu6icsXPhB76APP4SLF3H/5hvOt24NgwZB9eqglNE7qF8/KFmSnY0acSY0lLD5843eThZ4XD2Q0pLQYLtixQqOHDnCvHnzOHIkaVV7QoPtgQMHmDNnDoMHP+ic06dPn4d7nsXEwLp1MGEC9O4NERHUq1cvQ2nLgXhltLrPBI6KyNcWXZglkSK7X/pJIHtduSLSvbvxVFC79iPHMuVKMXExsujIImk5p6UwErEbbSdd53eVtSfXWlxdlJ1VDo/rbjM6Nlq2nN0io9ePlqazmor9aPuk7TEvIJTAnBp76q6p0vadtuZqmkWrF4lnZc+HquoSqmlq1qwptWrVSrWaRiQdVRuNG0ut4sWlV9GiUhckBEQ6dBAR0116uXIi27c/SM+RkXM8omrLkieBTD1xxMWJHD8up6dMEV83N5GffjI2uHfPmD0KjFHmBw6IiMjy5cvTnbYc2A00xpjv5QDGvO4hwLOiq4M0S/zvfyIlS4rY24uMGiUSHZ3TJXr8jl87LkNWDZESX5YQRiJVv6sqX2/9Wq7de/Q8BtlV5ZDZUdY/L/lZtuzYIr6+vjLyn5HCSESNVFLnhzoydPVQWX1itVy4dUF2nd8lCw4vkB92P5jv+JUlr0jg7EDz+4AfAoSRSPEvi0udH+pI5987y7sr35VJ2yfJsn+XycHLB+V21O1Mf26JxcfHS8UKFeTmsmUiW7can9veveKb0Ojs4CBSv77Im28aqbwzeo5kKTAs+d1Y3GD75psip049aExPNC/FaTCuJdFxZMuWTCcdFBE9YljLvC5d4KmnYPBgY8DZ4sUwa5Yx/3Fe4VXci3GtxjGm+Rj+d+R/TN09lXdXv8unmz7l/LvncbRzTHG/lKocduzYkWSbhCqHxo0bJ6lyKFWqlMXlS2/3wEJlC3Hy9EkuX77MwbsHeXnfy8x+ajYAz9d4Hh83H5p5NMO1oGuS85QpXIa6ZesmWTaj/Ywk70c8NYKjV48SeiOU0BuhHA0/yorjK7gfe9+8TdOKTdnQZwMA76x8B//S/rzs/zIAt6JuJanasHgMx1NPUSRx11YXF6haFT7/HHbsMF4zZxqjrp94gnq2thxfu5bTHh6UK12a4CNHmNuyJezbB7VrQ3g4NzZvpqC7Ow7lyjFjyRJz1VZ6GN+zSSmlYNs22LQJQkIYtmcPg//7D//p06nZtasx5qFiRahVy3iVLAnDhsGMRJ/1k0+mqxyPmw4CWhKurvDbb8askEFBxpzxH34IH330YFbIvMDRzpFefr3o5deLA5cPsP/SfnMA6LukL+2qtKOLTxfz9ql+ASSS1qAnS6QVbG5E3sDJ3YlBEwYRtT+KYyHHUGcUYWFhPOn3JNOfm45PASNgVHetTnXX6uk6f2LPVXuO56o9l2SZiHDl7hVzYHB2eDDSfOPZjRSwKwBAZGwkflP92NxvM5MnT6Z169bExcWlawwHQM+ePVm/fj1Xr17FffBgRo0axStffQWxscYLsCtRgsnPPkvr9euJu3SJfkWK4Lt5M9NmzYIdOwgqU4ajnTvzEmAL+AAzixSBLVugUSN6tmzJ+i1buBoVhbuLC6O6deOVbt2ML+dChYxnkPh43OPiOLdrFwwfDsePE1anjjHmYepU+OUXqFCBIrVqMev55yEgAGnf3hjzsG4dJASc0FAjx4s1ycjjQ1a/dHWQdbh2zch9BiJ+fiKmXF55WsT9CKk1tZZM2j5JRETuRN2RA5cOZFuVQ/L67ZmzZkrHlzrKh2s/lPrT64vNKBthGGJXx04KVygsAa0DpIZ/jXTXb2e10IhQKfx5YfGd4ptmVVuWu35dZONGo75z6lQj3fegQSKnTxvr580TKV3aSAGeUO0ERkZYEWO2Ozs7iQHxADllZydRNWuKn6+vMebh3DnztKipJd1LkJW/G3QWUS2rLF0qUqaM0X71f/+XYttcnhIfH2/uTjp9z3RhJFJoTCGxLW4rHiM8pM73dcS5vLMEfhUoLy58UV7/83U5c+OMREREyL6wffLT3p9k8tTJ0rt3bwm7GSYHLh2Q0xGnZe+RveLj6/PIc2/avEmaNm9qfl+8XXHhacR2lK00mtlIPvn7E9kYulGiYqPMZc1IsMkO606tE4cxDvLkzCflbvTdnC5O2uLijC/zY8dENm0ypjgVMbLBfvCByOzZMur118Xe3l7s7OzMNwWJG2xXrlwpzs7OUqBAAXFxcZHNmzebD//UU08lDOqSIkWKPDIFe0boIKBlqevXRV5+2fiLqVFDZNcuy/bLTE+XtPbNDlfvXpVJ2yfJ2yvellYft5JCZQqJU0knqdCpgvhN9ZMS3UqIcydnOXzlsGzdulVc3V2FEkjb9m3l+vXrMnztcKMnTg2EQgg2CEWQQl0Lice3HuI31U8azWwkEfcjRESk9/96i21xW3PDcIWqFWTyn5PlZuSDL/mcvNtMrwWHF4gaqaTd3HbmgXy5VWYa7S3ZN7N0ENCyxZ9/Gj3ZbG1Fhg9/9FOBtf+nyQp3ou7I6YjTEhcfJyIih68clvmH5svMvTPl223fypgNY2To6qEStCxIXlz4orSf116azW4m96KNGcj+Of2PDJk8JN3dAxP06NFDSpcuLXZ2dlKuXLnHfreZEd/v/F4YifT9o2+uHsGdmR5i6a1OzIiMBgGLWq2UUm2AiRjtKjNEZGyy9cWAn4DKQCTQT0QOWbKvlru0bWskpHvvPfjiC1iyxOhBZMo6nURmcsmfOnUqzX2tkbODc5LGUh83H3zcLC9zYKVAAgcGMm7guCTLE8+N0LBhQ44fP57i/vPmzUtnibPe6/Ve5/Ldy4zaMIqSziUZ2yJ3fgVkpoeYJfvmlDRHDCulbIEpwDMYDes9lVLJ/6o/BEJExA94CeNL39J9tVzGxcXoobdiBdy6BQ0bwgcfQGRk0u0yM5LTkn213GPEUyMICgjiyy1f8s22b3K6OBli3GwnlVIPsYiICPz9/fnuu+/MPcQs2TenWPIkUB84ISKnAJRSwUAHkqYm9QG+ABCRY0qpSkqpUoCnBftquVRCmur334evvoKlS+Gnn4ygAJnrVmnN/2m09FNKMfnZyYTfC+fcrXNp72CFMpMW+969e2num1MsyR1UDkj8WwszLUtsP9AZQClVH6gIuFu4L6b9BiildiuldoeHh1tWei3HFS0KP/4Iq1bBvXvQqBEMGQL372duxi9L9tVyF1sbW4K7BjOh1QQAYuNjc7hElrkfc5+VJ1ZS3rt8hnL6FClSxKJ8QDnFkiCQ0u1X8tu0sUAxpVQI8CawD2OGG0v2NRaK/CgidUWkrpubmwXF0qxJq1Zw8CC89pqRC8vfHwoWzFgiLGv/T6NlnJ2NHUopjoYfxWeKDzvCrKNePDX/Xv2XBjMa8Mxvz1B+Ynls29nydMun8fb2pnv37uaBbwmD344ePYqvry/Vq1dnxYoVTJw4EQA7OzvzoLnE+1qFtFqOgYbAqkTvhwPDH7G9AkKBIundN+GlewflbmvXGmNvChcWGTEi/YmwEqSUREvLGy7dviRNZzWVg5etN4f53ANzpdDnhaTElyVkTsgc+WzjZ9JiTguJjjWSan2x6QsJWhaUJT2e0uoefePGDWnXrp34+fmJj4+P/PTTT+beQcA7wGHgEDAPcJTMdBHFaDc4BXgADhhVP77JtnEBHEw/vwrMsXTflF46COR+Z88aGUmVEhk/3pwqXtPMEr484+Pjk4yDyGn3Y+5L0LIgYSTSaGYjOXfzXIrbDV09VLrN72Z+//aKt+Xbbd/K8WvHM3V+S7pHf/bZZzJ06FAREbly5YoUK1ZMMCaRKQecBpzE+A6eD/SRzHQRFZFYpdQgjEkKbIGfROSwUirItH4a4A3MUUrFYTT6vvKofS14QNFyufLljZxaL79stBEcPmykWClQIKdLplmLhIb+j//5mMXHFrOp7yaKOxXP0TKdijhFl/ldCLkUwtAnh/Jp80+xt7VPcdsvW35p/jk6Lpq1p9dy6Moh3l71NtVKVKNtlba0q9qOxhUap3qMlFjStVopxe3btxER7ty5Q/HixYmIiEioarcDnJRSMUBB4MIjT/ioCJFTL/0kkHfExYl8/LHxzNm48WPJmKvlMX+f+lscxjhIwxkNczy9xKnrp6TyxMqy7N9lGdr/5PWTMmn7JGn9S2vzvNdFvigi3eZ3k9n7Zkv43fA0j2FJyupbt25JYGCglC5dWpydneXPP/9MXB00GLgDhAO/SRrft3pmMS1L2djA6NEwbx7s3m0MKks04Zam0cyjGXM7z2V72Ha6L+hOTFz2Tm8XFRvF9D3TjS6dxTw4NugY7aq2S3vHFHgW8+TNBm+ystdKrg29xh/P/0F3n+5sPruZPkv6sOrEKgAu37nMvov7UuwKndKy5N2jV61ahb+/PxcuXCAkJIRBgwYB2JgG7nbAqIIvCzgrpXo9qsw6CGjZokcP2LABoqKMcQR//pnTJdKsSRefLkxtO5Xlx5fz6rJXU/wizCrzD89nwJ8D2HDGmBfBzubxZNgv5FCIDtU7ML39dM6/e549A/aYg8vcg3Op82Mdztw8A0DYrTDuRt8FLOtaPWvWLDp37oxSCi8vLzw8PAAcgRbAaREJF5EYYBHwyAkLdBDQsk39+rBrF1SrBu3bw7hxRs5eTQN4re5rjAocxc/7f+aDtR9k+fmu3L0CQC+/Xmztt5XASoFZdi6lFHXK1KGoY1EAXvR7kQXdFlDJpRIA76x6hxJflWBH2A6LukdXqFCBdevWAXD58mX+/fdfgGjgLPCEUqqgab7hp4GjjyxcWvVFOfHSbQJ52927It26Ge0EL7+c91NTa5aLj4+XgcsHCiOR8VvGZ8k5omOj5d2V74rLWBc5HXE6S86RXhtDN8p7q94zt4mkNcfw+fPnpWXLllKjRg3x9fWVX375JXGbwCjgGEYX0V+AAvKI71slVngrVrduXdm9e3dOF0PLQiJGW8HIkcYo40WLjJn3NC0uPo6eC3uy4MgC5naeS8+aPR/bsc/ePMvz/3ue7WHbGVRvEONbjTfPhpbbKaX2iEjdtLdMSk8vqeUIpYx5jL29jW6k9esbuYf8/HK6ZFpOs7Wx5ZdOv1DYoTD1ytV7bMdd/t9yXvrjJWLiYpjfdT7dfLs9tmPnZrpNQMtR3bsb4wliYowpXZcsyekSadaggF0BZnaYiVdxL0SE0BuhGT5WbHwsw9YOo928dlQoWoE9A/boAJCIDgJajqtb12gw9vGBTp1g7FjdYKw98Mk/nxDwYwAXbj96zFNKzt86T7Ofm/Hlli95LeA1tr2yjSolqmRBKXMvHQQ0q1C2rNGF9PnnYfhwo4oo+fwEWv7Ut3ZfhjUaRplCZVLdZuXKlVSrVg0vLy/GjjUmrbkfc58GMxqw7+I+preczvkfztMgoAG+vr7mdM9gJDHs2rUr1atXx9vbm23btmX5NVmVR7Ua59RL9w7Kv+LjRUaPNnoONWwoculSTpdIsybHwo/J1btXkyx7VK6d3w78JkfDj6aYaydhnuaXXnpJpk+fLiIiUVFREhERkX0X9BiRwekl9ZOAZlWUgo8/hgULICQE6tUz/k1LSneCid28eZPnnnuOWrVq6TvBXCoyNpKn5zxN27ltzQOrIGmunTtxd7hT9Q6fT/8cgBdqvkB11+op5tqxs7Pj1q1bbNy4kVdeeQUABwcHXFxccuLycowOAppV6toVNm+G+HijC+nixalvGxcXx8CBA1mxYgVHjhxh3rx5HDmSdPK6KVOm4OPjw/79+1m/fj3vvfeeeR6DwYMH06ZNG44dO8b+/fvx9vbOykvTMsjRzpHJz05m14VddFvQzZxeIvFUpIUcCuFQzIErl64k2XfQoEEcPXqUsmXLUrNmTSZOnIiNjQ2nTp3Czc2Nvn37Urt2bfr378/du3cfOndepoOAZrXq1DEajGvUgM6d4fPPU24wTnwn6ODgYM66mJi+E8wbOlbvyLS201hxYgWvLH2FeIknLi6Og1cOcv3+dRxsHRjeeDjVXKsl2S+lXDu3bt0iNjaWvXv38vrrr7Nv3z6cnZ1TfJLMy3QQ0KxamTKwfj288AJ89BH07p2xCe31nWDe8WrAq4xpNoZfDvzCm3+9yfjD49l5ZCe/HvgVMP4eLMm1c+zYMdzd3XF3d6dBgwYAdO3alb1792b7NeUkHQQ0q+fkBL/+Cp99Br/9BoGBcOnSg/WSwayL+k4w9/qoyUcMqjeI73d/z0G7g7hGutLOrV26cu14enpSunRpypcvn5B7h3Xr1iXJ258f6BHDWq6gFHz4oTHCuFcvo8F46VKoXdvyrIvDhg176E6wQoUKD90J6iBg/ZRSTHxmItVdq9O0YlPO1T1HmzZtiIuLo1+/fua5fwGCgoL4+OOP6dOnDzVr1kRE+PLLL3F1dQXgu+++48UXXyQ6OhpPT88knQbyhYx0Kcrql+4iqj3Kvn0i5cuLFCwo8vPPIpGRMeLh4SGnTp0ydxE8dOhQkn2CgoJkxIgRIiJy6dIlKVu2rISHGxN8NG7cWI4dOyYiIiNGjJAhQ4Zk5+Vo2mNBBruI6gRyWq506ZLRWLxtG1SqBM2b/8WGDW8jYtwJfvTRR0nuBC9cuECfPn24ePEiIsKwYcPo1cuYayMkJIT+/fsnuRMsVqxYDl6dpqVfRhPIWRQElFJtgIkY8wTPEJGxydYXBX4FKmBUMY0XkVmmdaHAbSAOiLWkkDoIaJaIjTVyDU2caOQfKlgQXnoJ3nrLqDbStPwko0EgzYZhpZQtMAV4BvABeiqlkrecDASOiEgtIBCYoJRySLS+mYj4Z6SAmpYaOzvo0gU2boS9e42UE7NmGTmIWrWC5cuNcQaapqXOkt5B9YETInJKRKKBYIw5LBMToLBpJptCwHUg9rGWVNMeoXZt+OknOHcOPv0UDh+Gdu2MWcwmTYJbt3K6hJpmnSwJAuWAc4neh5mWJTYZ8AYuAAeBwSKScA8mwGql1B6l1IDUTqKUGqCU2q2U2h0eHm7xBWhaYm5uxniC0FBjcntXVxg8GNzdjX9PnMj4sTOTmgKMkc21a9emXbuMTWKuaVnBkiCgUliWvCGhNRCCMbu9PzBZKVXEtK6RiNTBqE4aqJRqmtJJRORHEakrInXd3NwsKbumpcre3pjcfts22LHDmNN46lSoWtV4QlizJn3pqjObmgJg4sSJOiWFZnUsCQJhQPlE790x7vgT6wssMvVUOgGcBqoDiMgF079XgMUY1Uualm3q1zcGm505YySn27XLaDPw9YVp08CSAcKZSU0BxtiF5cuX079//6y4RE3LMEuCwC6gilLKw9TY2wNYmmybsxiz2qOUKgVUA04ppZyVUoVNy52BVhiTH2tatitTBkaNgrNn4eefjZHIr79uVBW9/75RhZSazKSmAHj77bf56quvzO81zVqk+RcpIrHAIGAVcBSYLyKHlVJBSqkg02ZjgCeVUgeBdcAHInIVKAVsVkrtB3YCy0VkZVZciKZZqkABoyvp7t1GptJWreCbb6ByZWPswfr1D1cVpdSV2tLUFH/++SclS5YkICAgC69K0zLGorQRIvIX8FeyZdMS/XwB4y4/+X6ngFqZLKOmZQmljDTVjRoZvYqmToUffzTSVvv5GeMNXnjBeGLITGqKLVu2sHTpUv766y8iIyO5desWvXr14tdff83uS9a0h+hnU00Dypc3UlWfOwczZhhPAv37G8s//BAqVqzH8ePHOX36dLqTlH3xxReEhYURGhpKcHAwzZs31wFAsxo6CGhaIk5O8MorsH8//PMPNGkCX34J/v52vPTSZFq3bo23tzfdu3c3JylLSE/x8ccfs3XrVmrWrMnTTz+dJEmZplkrnTtI09Jw+DC8+KIRGAYOhHHjjGChadYky9JGaFp+5+trjDV45x2YMgXq1jUCgqblBToIaJoFChSAr7+GVavg+nVj7ME33+jcRFrup4OApqVDq1Zw4AC0aQPvvgvPPAMXL+Z0qTQt43QQ0LR0cnODP/4wupRu2mR0J12afPikpuUSOghoWgYoBUFBsGePMeK4Qwdj9PG9ezldMk1LHx0ENC0TvL1h+3Z47z0jD1HduhASktOl0jTL6SCgaZlUoACMH29kJr1xw2g0njDB+huNM5Mau1+/fpQsWZIaNWpkZ5G1LKCDgKY9Ji1aGI3Gzz4LQ4ZA69ZwIXm+XSuR2dTYffr0YeVKnQYsL9BBQNMeI1dXI/fQDz/Ali1Go/Eff+R0qR6W2dTYTZs2pXjx4jlRdO0x00FA0x4zpWDAAGPe44oVoVMneO01y+YtyC6ZTY2t5R36N6ppWaR6dWNms6FDYfp0CAgwAkNaMlNXn9a+CTKTGlvLW3QQ0LQs5OBgJKBbuxZu34YnnjByD6XWaJyZunpL9k1gaWrszp07P5QaW8tbdBDQtGzQvLnRaNyunfFk0LIlJKt9ATJXV5/avnFxxqxp69YZ8yXMmwd162Y8NbaWt1g0qYymaZlXogQsXAgzZ8LgwUaj8YwZRptBgpTq6nfs2JHkOIMGDaJ9+/aULVuW27dv8/vvvxMVZcP27ecRKc8338DJk7Bxoztnz+5gxAiIiUlalrNn7Zg82UiNHRcXR79+/cypsQGCgoL4+OOP6dOnDzVr1kREkqTG7tmzJ+vXr+fq1au4u7szatQoXnnllaz54LQspYOApmUjpYzJapo2NWYt69zZeP/tt+Ds/Oi6+ogIOHEC5s5dRXS0P23a/M3hwyfp0KElcXFNAGPfNWugSBEoVgxcXRVduxpTZya8hg0zXgsWPMt//z2b5FxBQUHmn8uWLcvq1atTvI558+Y9ng9Ey3EWBQGlVBtgImALzBCRscnWFwV+BSqYjjleRGZZsq+m5UdVq8LWrTBihNFmsHEj/PorFCjgzoED5/jpJ+NuftmyMK5fL8tvvxlBwDALGMapU4rKlb1wc/PgueeOUa6cOytWnOPPP42njrFjw4CyDB+e9NyzZsGZM9C7N1SoYAxu0/IxEXnkC+PL+yTgCTgA+wGfZNt8CHxp+tkNuG7aNs19U3oFBASIpuUXf/8tUq6ciDGpZYyAh8ApsbGJEgcHP2nY8JAEBYmMHy+yeLFIt25BMnz4CBERuXTpkpQtW1bCw8MlJiZGPDw85NSpUxIVFSV+fn5y6NChFM95+bJIpUoipUqJnDmTfdeqZR1gt6Tx3ZrSy5IngfrACTEmjUcpFQx0ABJ3OxCgsDKeWwuZgkAs0MCCfTUtX2vWzGg0nj4dihSx49q1ycyc2RobG6Ou/qOPHtTVd+wYRP36qdfVp1TPn5KSJWH5cmjY0Gis3rzZqELS8p80p5dUSnUF2ohIf9P73kADERmUaJvCwFKgOlAYeF5Elluyb6JjDAAGAFSoUCHgzJkzj+P6NE17hDVrjDkRWrUy0mHb6VbCXCsrp5dUKSxLHjlaAyFAWcAfmKyUKmLhvsZCkR9FpK6I1HVzc7OgWJqmZVbLlvD997BihTFJjpb/WBL3w4Dyid67A8nTYvUFxprqpU4opU5jPBVYsq+maTlowAD47z8j82mVKvDmmzldIi07WfIksAuoopTyUEo5AD0wqn4SOws8DaCUKgVUA05ZuK+maTnsyy+NiXHefhv++iunS6NlpzSDgIjEAoOAVcBRYL6IHFZKBSmlEjoVjwGeVEodBNYBH4jI1dT2zYoL0TQt42xt4bffoFYteP55o6Fayx/SbBjOCXXr1pXdu3fndDE0Ld85fx4aNAAbG9ixA8qUyekSaZbKyoZhTdPyiXLlYNkyuH4d2rfXcybnBzoIaJqWRO3aRpK5PXuMUcXWPk2mljk6CGia9pDnnjN6Cy1aBB9+mNOl0bKSHhqiaVqK3n7b6Dr65ZdG11GdJDRv0kFA07QUKQWTJsGpUxAUBB4exrwIWt6iq4M0TUuVvT3Mn29kPe3SBfTEYnmPDgKapj1S0aJGsjkHB2jbFq5ezekSaY+TDgKapqWpUiVYssQYR9CpE0RF5XSJtMdFBwFN0yzyxBPw889G2un+/Y3ZD7TcTzcMa5pmseefN6a4/L//M9oJPv44p0ukZZYOApqmpcuHHxpdRz/5BLy8oGfPnC6Rlhm6OkjTtHRRCn78EZo2hb59jbmStdxLBwFN09KtQAFjNHH58tCxozGWIDUrV66kWrVqeHl5MXbs2IfWjxs3Dn9/f/z9/alRowa2trZcv34dgEqVKlGzZk38/f2pWzfdudE0C+gsopqmZdi//xrzFJcubTwRuLgkXR8XF0fVqlVZs2YN7u7u1KtXj3nz5uHj45Pi8ZYtW8Y333zD33//DRhBYPfu3eY5lLXU6SyimqZlu2rVjCeC48ehWzeIiUm6fufOnXh5eeHp6YmDgwM9evRgyZIlqR5v3rx59NSNDNlKBwFN0zIlMNBoI1i7FgYOTNp19Pz585Qv/2CGWXd3d86fP5/ice7du8fKlSvp0qWLeZlSilatWhEQEMCPP/6YVZeQr+neQZqmZVrfvsbTwBdfGE8H771nLE+pulkpleIxli1bRqNGjShevLh52ZYtWyhbtixXrlyhZcuWVK9enaZNm2bJNeRX+klA07TH4tNPoWtXeP99+OMPY5m7uzvnzp0zbxMWFkbZsmVT3D84OPihqqCEbUuWLEmnTp3YuXNnlpQ9P7MoCCil2iil/lVKnVBKDUth/ftKqRDT65BSKk4pVdy0LlQpddC0Trf2aloeZWMDc+ZAvXrw4ouwaxfUq1eP48ePc/r0aaKjowkODqZ9+/YP7Xvz5k02bNhAhw4dzMvu3r3L7du3zT+vXr2aGjVqZNv15BdpVgcppWyBKUBLIAzYpZRaKiJHErYRkXHAONP2zwHviMj1RIdpJiI67ZSm5XFOTkaOoQYN4MknoX9/O0aNmkzr1q2Ji4ujX79++Pr6Mm3aNACCgoIAWLx4Ma1atcLZ2dl8rMuXL9OpUycAYmNjeeGFF2jTpk32X1Qel2YXUaVUQ2CkiLQ2vR8OICJfpLL9XOAfEZlueh8K1E1PENBdRDUtd7t0CUaPhunTwc4O3nwTPvgASpTI6ZLlXVnZRbQccC7R+zDTspQKURBoAyxMtFiA1UqpPUqpAamdRCk1QCm1Wym1Ozw83IJiaZpmrUqXhu+/N8YRdOsG48eDpyeMGQOmGh7NSlgSBFJqyk/t8eE5YEuyqqBGIlIHeAYYqJRKsWlfRH4UkboiUtfNzc2CYmmaZu08PY12ggMHjFnJPvnEWPbNNxAZmdOl08CyIBAGlE/03h24kMq2PYB5iReIyAXTv1eAxUD99BdT07TcrEYNWLwYduwAf394911j3uIZMyA2NqdLl79ZEgR2AVWUUh5KKQeML/qlyTdSShUFngKWJFrmrJQqnPAz0Ao49DgKrmla7lO/PqxZA+vWQbly8Oqr4OMDwcEQH5/Tpcuf0gwCIhILDAJWAUeB+SJyWCkVpJQKSrRpJ2C1iNxNtKwUsFkptR/YCSwXkZWPr/iapuVGzZvDtm1GT6ICBYx01HXqGNNYWmE6szzNonECIvKXiFQVkcoi8plp2TQRmZZom9ki0iPZfqdEpJbp5Zuwr6ZpmlLQvj2EhMCvvxoNxu3aQePGsGFD+o6V0UylkZGR1K9fn1q1auHr68uIESMez8XlJiJida+AgADRNC1/iY4WmTZNpGxZERBp3Vpk9+6094uNjRVPT085efKkREVFiZ+fnxw+fDjV7ZcuXSrNmjUTEZH4+Hi5ffu26fzRUr9+fdm2bdtjuZ7sBuyWDHzf6rQRmqZZBXt7eO01Y/rKceOMEcd16xqpKI4eTX2/zGQqVUpRqFAhAGJiYoiJiUk1t1FepYOApmlWxckJhgyB06dhxAhYtcroXdS3L4SGPrx9ZjOVxsXF4e/vT8mSJWnZsiUNGjR43Jdk1XQQ0DTNKhUpAiNHGrOWvf02zJtnTG7/5pvGiOQEkslMpba2toSEhBAWFsbOnTs5dCh/dWDUQUDTNKvm5gYTJhjVRH37wtSpULmyMeF9RETmM5UmcHFxITAwkJUr81cHRh0ENE3LFdzd4YcfjPaBDh2MuQs8POD8+YxnKg0PD+fGjRsA3L9/n7Vr11K9evXsuiSroIOApmm5SpUqMHeu0bW0WjXo2dOOl14yMpV6e3vTvXt3c6bShGylkHKm0osXL9KsWTP8/PyoV68eLVu2pF27djlwVTlHTzSvaVqudfMmPP00HDoEf/4JLVrkdIlyjp5oXtO0fKdoUaP3UNWqRhXRpk05XaLcRwcBTdNytRIljEnuK1SAtm1Bz0CZPjoIaJqW65UsaQQCNzdo3dpoL9Aso4OApml5QrlyRnbSwoWhZUs4ciTtfTQdBDRNy0MqVTICgZ2d0WB8/HhOl8j66SCgaVqeUqWKEQhiY41AcOZMTpfIuukgoGlanuPjA6tXG+mpmzeHVFIJaYBdThfAUjExMYSFhRGpJybNFo6Ojri7u2Nvb5/TRdG0DKldG1auNNoHWrQw5igoWTKnS2V9ck0QCAsLo3DhwlSqVCnfpXrNbiLCtWvXCAsLw8PDI6eLo2kZ1qCBMVtZ69ZGMPjnH0iUO04jF1UHRUZGUqJECR0AsoFSihIlSuinLi1PaNIEli6Ff/81gsHNmzldIutiURBQSrVRSv2rlDqhlBqWwvr3lVIhptchpVScUqq4Jfumhw4A2Ud/1lpe0qIF/O9/xviBZ5+FO3dyukTWI80goJSyBaYAzwA+QE+llE/ibURknIj4i4g/MBzYICLXLdlX0zQtO7RrZ8xJsH27kWLi/v2cLpF1sORJoD5wQoxJ46OBYKDDI7bvCczL4L5WzdbW1jxR9XPPPWdOQZvgzp071K1bF09PTy5cuJBk3Ysvvki1atWoUaMG/fr1IyYmxrzujz/+YPTo0QCMHDmS8ePHP3Tuzz77DF9fX/z8/PD392fHjh106tQJf39/vLy8KFq0qHki7a1btxIYGEiFChWSTLjRsWNH81R64eHhtGnT5nF9NJqWK3TtCj//bLQNdOkCUVE5XaKcZ0kQKAecS/Q+zLTsIUqpgkAbYGEG9h2glNqtlNodHh5uQbGyn5OTEyEhIRw6dIjixYszZcoU87rY2Fi6d+9O7969GTduHB06dODWrVvm9S+++CLHjh3j4MGD3L9/nxkzZpjXffXVV7zxxhupnnfbtm38+eef7N27lwMHDrB27VrKly/P4sWLCQkJYcaMGTRp0oSQkBBCQkJ48sknAWOSjC1btgBw48YNLl68aD6mm5sbZcqUMa/XtPyiVy+YNg1WrICePY3xBPmZJb2DUqocTi3/9HPAFhG5nt59ReRH4EcwUkk/qkBvv/34c4P4+8O331q+fcOGDTlw4ID5/WuvvcYzzzzDm2++CRhPDQkTXtvb2/Pss8+at61fvz5hYWEA/PfffxQoUABXV9dUz3Xx4kVcXV0pUKAAwCO3TaxHjx4EBwfTuHFjFi1aROfOnTl8+LB5fceOHfntt99o1KiR5ReuaXnAgAFGddDbb8PLL8OcOWBrm9OlyhmWPAmEAeUTvXcHLqSybQ8eVAWld99cIy4ujnXr1iWZvWjmzJnmAADGF+xff/31UD/7mJgYfvnlF3NVzJYtW6hTp84jz9eqVSvOnTtH1apVeeONN9iwYYNF5Xz66afZuHEjcXFxBAcH8/zzzydZX7duXTbp3LtaPjV4sDE72dy58NprEB+f0yXKGZY8CewCqiilPIDzGF/0LyTfSClVFHgK6JXefdMrPXfsj9P9+/fx9/cnNDSUgIAAWrZsme5jvPHGGzRt2pQmTZoAxl2+m5vbI/cpVKgQe/bsYdOmTfzzzz88//zzjB07lj59+jxyP1tbWxo3bszvv//O/fv3qVSpUpL1JUuWfKjtQtPyk2HDjCeC0aPByQkmTYL81jEuzScBEYkFBgGrgKPAfBE5rJQKUkoFJdq0E7BaRO6mte/jvIDslNAmcObMGaKjo5O0CVhi1KhRhIeH8/XXXyc5piX98W1tbQkMDGTUqFFMnjyZhQsXprkPGFVCb775Jt27d39oXWRkJE5OTpZfgKblQSNHwpAhMHkyfPABWOFki1nKonECIvKXiFQVkcoi8plp2TQRmZZom9ki0sOSfXO7okWLMmnSJMaPH5+kl8+jzJgxg1WrVjFv3jxsbB587N7e3pw4ceKR+/77778cT5QOMSQkhIoVK1p03iZNmjB8+HB69uz50Lr//vuPGjVqWHQcTcurlIKvvoI33oBx42DUqNS3XblyJdWqVcPLy4uxY8emuM369evx9/fH19eXp556yrz8m2++wdfXlxo1atCzZ0/rGYwpIlb3CggIkOSOHDny0LLs5uzsnOR9u3btZM6cORbta2trK56enlKrVi2pVauWjBo1SkRE7t69Kz4+PhIfHy8iIiNGjJCiRYtKuXLlzK/du3dLw4YNxdvbW2rWrCmdOnWS8PBw87H/+ecfadu2bZLzPfXUU7Jr165HXsO4ceNk0qRJqZbZGj5zTcsucXEiffuKgMjYsQ+vj42NFU9PTzl58qRERUWJn5+fHD58OMk2ERER4u3tLWfOnBERkcuXL4uISFhYmFSqVEnu3bsnIiLdunWTWbNmPdbyA7slA9+3uSZ3kDW4k2yY4bJlyyzeNzaVfmgFCxakRYsWrFu3jhYtWjBy5EhGjhz50HZbt25N9diBgYEEBgYmWbZ+/foUt018DUuXLmXJkiVpll3T8gMbG5g+3WgjGDYMChaERH092LlzJ15eXnh6egKYe//5+DwY/zp37lw6d+5MhQoVAKPdLUFsbCz379/H3t6ee/fuUbZs2ey5sDTkmtxBedmHH37IvXv3svWc4eHhvPvuuxQrVixbz6tp1szW1ugu2rEjvPUWJBrOw/nz5ylf/kFnR3d3d84ny1H933//ERERQWBgIAEBAcyZMweAcuXKMWTIECpUqECZMmUoWrQorVq1yo5LSpMOAlagVKlSSbqbZgc3Nzc6duyYrefUtNzA3h6Cg6FNG2M8wW+/GcslhRbj5Dm2YmNj2bNnD8uXL2fVqlWMGTPGHBiWLFnC6dOnuXDhAnfv3uXXX3/NjstJk64O0jRNS6ZAAVi0CNq2NQaTOToad/7nzj1IgBAWFvZQlY67uzuurq44Ozvj7OxM06ZN2b9/PwAeHh7m7uCdO3dm69at9OrVi5ymnwQ0TdNS4ORkpKBu0MBIL3H+fD2OHz/O6dOniY6OJjg4+KEn+A4dOrBp0yZiY2O5d+8eO3bswNvbmwoVKrB9+3bu3buHiLBu3Tq8vb1z6MqS0k8CmqZpqShUCP76y6ga6tnTjvffn0zr1q2Ji4ujX79++Pr6Mm2a0VM+KCgIb29v2rRpg5+fHzY2NvTv39/cDbtr167UqVMHOzs7ateuzYABA3Ly0sxUSvVcOa1u3bqye/fuJMuOHj1qNZEzv9CfuaYZbt0y5iHYvh1+/RV6PDQiKucppfaISN307qerg9Ihu1NJR0ZG0rJlS0Y9avRKIgsWLMDX1xcbGxsSB9GDBw+mmWJC07TUFSlizFfcuDG8+CL88ktOl+jx0UEgHbIzlXR0dDRdunQhICCAESNGWFS+GjVqsGjRIpo2bZpkec2aNQkLC+Ps2bMZuWxN0zCqhpYvh8BAo7H4p59yukSPR65tEwicHZjmNu2qtmPIk0PM2/fx70Mf/z5cvXeVrvO7Jtl2fZ/16Tp/VqaSjo2NpUePHlSpUiXVoekpeVTVzXPPPUdwcDBDhw61+HiapiXl7Ax//mmMI3jlFWMuAiup2s8w/SSQAVmdSvqrr77Czs6Ob5OlS23SpIl59rDEr7Vr16ZZZp02WtMeDycnWLLE6D762muQzjySVifXPgmk98498fauBV3TvT9kXyrpxo0bs23bNv777z+qVq1qXp6ZL3GdNlrTHh9HR1i4EJ5/HgYNgpgYY4Ka3Eg/CaRDdqWSbtq0Kd9++y3PPPNMki/uzDwJ6LTRmvZ4FSgA8+dD587wzjtGBtLcKNc+CeSkhFTSHTp04PXXX3+oyiclCamk161b91Aq6ZSGj3fp0sU8GfzGjRtxcXHJ1JOAThutaY+fg4ORYqJ3bxg61Hgi+PDDnC5V+ugngQyqXbs2tWrVIjg42KLtg4KCuHz5Mg0bNsTf39/cJbRp06bs27cvxbwkQUFBdO7cmfbt21uUe3zx4sW4u7uzbds22rZtS+vWrc3r/vnnH9q2bWvh1WmaZil7e2PswIsvwkcfGfMRWOHwq1TpwWJWYPDgwTz33HO0aNEiS44fFRXFU089xebNm7Gzs/zhLy9/5pr2uMXFQf/+MHu2EQzGjMneqSozOlhMVwdZgQ8//JAdO3Zk2fHPnj3L2LFj0xUANE1LH1tbmDkT7Ozgs8+MqqGxY61/zmKLvhWUUm2AiYAtMENEHuq8rpQKBL4F7IGrIvKUaXkocBuIA2IzEqnyuqxOJV2lShWqVKmSZcfXNM1gYwM//GBUEX31lREIJkyw7kCQZhBQStkCU4CWQBiwSym1VESOJNrGBfgeaCMiZ5VSJZMdppmIXH18xdY0TbNONjbG2AF7e/jmGyMQTJpkvYHAkieB+sAJETkFoJQKBjoARxJt8wKwSETOAojIlcddUE3TtNxCKfj2WyMQTJhgBILvvzcChLWxJAiUA84leh8GNEi2TVXAXim1HigMTBSROaZ1AqxWSgnwg4j8mNJJlFIDgAGAeX5OTdO03EopY+yAvb3RNhAbCz/+aH2BwJIgkNJDTPIuRXZAAPA04ARsU0ptF5H/gEYicsFURbRGKXVMRDY+dEAjOPwIRu+g9FyEpmmaNVIKPv/cGE8werTxRPDTT0YjsrWwJCaFAeUTvXcHkucfCANWishdU93/RqAWgIhcMP17BViMUb2UK+XWVNJr1qwhICCAmjVrEhAQwN9//21e16JFCyIiItL1OWiaZjmljLEDo0cbk9j37m08FVgLS4LALqCKUspDKeUA9ACWJttmCdBEKWWnlCqIUV10VCnlrJQqDKCUcgZaAYceX/GzV25NJe3q6sqyZcs4ePAgP//8M7179zav6927N99//326PgdN09Lv44/hiy9g3jx44QXjqSA1K1eupFq1anh5eaWaSXj9+vX4+/vj6+vLU089BYBSylEptVMptV8pdVgpleYdZJrVQSISq5QaBKzC6CL6k4gcVkoFmdZPE5GjSqmVwAEgHqMb6SGllCewWBnN4nbAXBFZmdY5LRIY+PCy7t3hjTfg3j1jGqDk+vQxXlevQtekqaRZvz5dp89NqaRr165t/tnX15fIyEiioqIoUKAA7du3p0mTJnz00UcWn0fTtIwZNsyoGnrvPeNpIDjYeJ9YXFwcAwcOZM2aNbi7u1OvXj3at2+Pj4+PeZsbN27wxhtvsHLlSipUqMCVK1coVaoUQBTQXETuKKXsgc1KqRUisj21Mlk0TkBE/gL+SrZsWrL344BxyZadwlQtlJckpJJ+5ZVXzMtmzpyZZJuOHTvSsWPHh/ZNSCU9ceJEIPVU0i1atEgxlfTt27cfOub48eMtHm28cOFCateuTYECBQAoVqwYUVFRXLt2jRIlSlh0DE3TMu7dd40BZYMHG/eiCxYYyegS7Ny5Ey8vLzw9PQHMN5OJg8DcuXPp3LmzuRNNyZJGr3wxUkDcMW1mb3o9so019w4hfdSde8GCj17v6pruO3/I3amkAQ4fPswHH3zA6tWrkyxPSDOtg4CmZY+33jJ6Db3xBnTqBIsWGempAc6fP0/58g+aYd3d3R/KKPDff/8RExNDYGAgt2/fZvDgweZ1prFdewAvYIqIPDIdQe4NAjkgoU3g5s2btGvXjilTpvDWW29ZvH9CKukffvghyTFv3ryZZLumTZvy8ssv88wzz7Bp0ybKli0LZO5JICwsjE6dOjFnzhwqV66cZJ1OM61p2e/1141AMGAAtG8Pf/xh3L+mlM9NJRtpFhsby549e1i3bh3379+nYcOGAAUARCQO8DcN4l2slKohIqm2xeogkAG5LZX0jRs3aNu2LV988QWNGjVKsk5EuHTpEpUqVcrQsTVNy7j+/Y1A0LcvtGsHy5YZd/7nzj0YmhUWFma+EUzg7u6Oq6srzs7OODs707RpU06cOFEw8TYicsM0dqsNj+iQY2XDFnKP3JRKevLkyZw4cYIxY8aYJ6K5csUY1L1nzx6eeOIJnVxO03LIyy/DL7/Ahg3wzDPg41OP48ePc/r0aaKjowkODn4ot1iHDh3YtGkTsbGx3Lt3L6G66L5Sys30BIBSygloARx7ZAFExOpeAQEBktyRI0ceWpZXvPXWW7JmzZocO/fatWtTXJeXP3NNszbBwSKvvCISFyeyfPlyqVKlinh6esqnn34qIiJTp06VqVOnmrf/6quvxNvbW3x9feWbb74RYDfgB+zD6Kl5CPhE0vi+1fMJWIHLly+zY8eOLM0kmprp06fz6quvprguL3/mmpbXZHQ+AV0dZAWyOpX0o6QWADRNyx9yVRCwxqeWvEp/1pqWP+SaIODo6Mi1a9f0l1M2EBGuXbuGY0LHZU3T8qxc0yXE3d2dsLAwwsPDc7oo+YKjoyPu7u45XQxN07JYrgkC9vb2eHh45HQxNE3T8pRcUx2kaZqmPX46CGiapuVjOghomqblY1Y5WEwpFQ6csXBzV+BqFhbncckt5QRd1qyiy5o1dFkNFUXELe3NkrLKIJAeSqndGRkll91ySzlBlzWr6LJmDV3WzNHVQZqmafmYDgKapmn5WF4IAj/mdAEslFvKCbqsWUWXNWvosmZCrm8T0DRN0zIuLzwJaJqmaRmkg4CmaVo+lmuDgFKqjVLqX6XUCaXUsJwuT2JKqZ+UUleUUocSLSuulFqjlDpu+rdYTpYxgVKqvFLqH6XUUaXUYaXUYNNyqyqvUspRKbVTKbXfVM5R1ljOxJRStkqpfUqpP03vrbKsSqlQpdRBpVSIUmq3aZm1ltVFKfU/pdQx099sQ2ssq1KqmunzTHjdUkq9bY1lzZVBQCllC0wBngF8gJ5KKZ+cLVUSszEmd05sGLBORKoA60zvrUEs8J6IeANPAANNn6W1lTcKaC4itQB/oI1S6gmsr5yJDQaOJnpvzWVtJiL+ifqwW2tZJwIrRaQ6UAvj87W6sorIv6bP0x8IAO4Bi7HCsub4fMIZeQENgVWJ3g8Hhud0uZKVsRJwKNH7f4Eypp/LAP/mdBlTKfcSoKU1lxcoCOwFGlhrOQF3jP/kzYE/rflvAAgFXJMts7qyAkWA05g6tFhzWZOVrxWwxVrLmiufBIBywLlE78NMy6xZKRG5CGD6t2QOl+chSqlKQG1gB1ZYXlP1SghwBVgjIlZZTpNvgaFAfKJl1lpWAVYrpfYopQaYllljWT2BcGCWqZpthlLKGessa2I9gHmmn62urLk1CKgUlum+rpmglCoELATeFpFbOV2elIhInBiP1+5AfaVUjRwuUoqUUu2AKyKyJ6fLYqFGIlIHo3p1oFKqaU4XKBV2QB1gqojUBu5iDdUpj6CUcgDaAwtyuiypya1BIAwon+i9O3Ahh8piqctKqTIApn+v5HB5zJRS9hgB4DcRWWRabLXlFZEbwHqMdhdrLGcjoL1SKhQIBporpX7FOsuKiFww/XsFo966PtZZ1jAgzPQECPA/jKBgjWVN8AywV0Qum95bXVlzaxDYBVRRSnmYIm0PYGkOlyktS4GXTT+/jFH3nuOUUgqYCRwVka8TrbKq8iql3JRSLqafnYAWwDGsrJwAIjJcRNxFpBLG3+bfItILKyyrUspZKVU44WeM+utDWGFZReQScE4pVc206GngCFZY1kR68qAqCKyxrDndKJGJxpZngf+Ak8BHOV2eZGWbB1wEYjDuXl4BSmA0FB43/Vs8p8tpKmtjjKq0A0CI6fWstZUX8AP2mcp5CPjEtNyqyplCuQN50DBsdWXFqGffb3odTvi/ZI1lNZXLH9ht+jv4AyhmxWUtCFwDiiZaZnVl1WkjNE3T8rHcWh2kaZqmPQY6CGiapuVjOghomqblYzoIaJqm5WM6CGiapuVjOghoVkspFWfKwHhIKbVAKVUwE8earZTqavp5xqMSDiqlApVSTyZ6H6SUeimj5050HBul1CTT9RxUSu1SSnmY1n2Y2eNrWkboIKBZs/tiZGKsAUQDQYlXmrLJppuI9BeRI4/YJBAwBwERmSYiczJyrmSeB8oCfiJSE+gE3DCt00FAyxE6CGi5xSbAy3SX/o9Sai5w0JRUbpzprvqAUuo1MEZCK6UmK6WOKKWWkyhRl1JqvVKqrunnNkqpvcqYp2CdKYleEPCO6SmkiVJqpFJqiGl7f6XUdtO5Fifkgzcd80tlzHnwn1KqSQrXUAa4KCLxACISJiIRSqmxgJPpfL+ZjtfLdKwQpdQPCQFPKXVHKTXBVOZ1Sim3LPm0tXxDBwHN6iml7DBysBw0LaqPMbLVB2M09k0RqQfUA141VbF0AqoBNYFXSXRnn+i4bsB0oIsY8xR0E5FQYBrwjekpZFOy3eYAH4iIn6k8IxKtsxOR+sDbyZYnmA88Z/pin6CUqg0gIsN48NTzolLKG+OpoZEYCfPigBdNx3DGyEVTB9iQynk0zWJ2OV0ATXsEJ1PqaDCeBGZifJnvFJHTpuWtAL+E+n6gKFAFaArME5E44IJS6u8Ujv8EsDHhWCJy/VGFUUoVBVxEZINp0c8kzQ6ZkHxvD8Z8EkmISJgp701z02udUqqbiKxLtunTGBOR7DJSO+HEg0Rj8cDvpp9/TXROTcsQHQQ0a3bfdCdsZvpSvJt4EfCmiKxKtt2zpJ1eXFmwTXpEmf6NI5X/WyISBawAViilLgMdMXLIJC/XzyIy3IJz6rwvWqbo6iAtt1sFvG5Kh41SqqopG+ZGoIepzaAM0CyFfbcBTyXqoVPctPw2UDj5xiJyE4hIVN/fG6NKxiJKqTpKqbKmn20wkuKdMa2OSbgGjKDQVSlVMqFcSqmKpnU2QMJTzwvAZkvPr2kp0U8CWm43A6PqZa8pLXY4xt31Yowql4MY2WYf+rIWkXBlzKS1yPSlfAVjas1lwP+UUh2AN5Pt9jIwzdRd9RTQNx1lLQlMV0oVML3fCUw2/fwjcEAptdfULvB/GLN92WBkox2IETDuAr5KqT3ATYy2A03LMJ1FVNNyEaXUHREplNPl0PIOXR2kaZqWj+knAU3TtHxMPwlomqblYzoIaJqm5WM6CGiapuVjOghomqblYzoIaJqm5WP/D0VuOtEWXyENAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric_triple_plot(x1,x2,x3,r2_list,r2_list1,r2_list12,metric='R^2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 补全"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step = 12\n",
      "Step = 36\n"
     ]
    }
   ],
   "source": [
    "# FC \n",
    "word_len = 1\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/'\n",
    "mae_FC_list = []\n",
    "mse_FC_list = []\n",
    "mape_FC_list = []\n",
    "rmse_FC_list = []\n",
    "evs_FC_list = []\n",
    "r2_FC_list = []\n",
    "# for step in [1,6,12,18,24,30,36,42,48,54,60,66,72]:\n",
    "for step in [12,36]:\n",
    "    print(f'Step = {step}')\n",
    "    file = f'FC_Complement_{step}_0103.csv'\n",
    "    input_path = folder_path + file\n",
    "    df = pd.read_csv(input_path)\n",
    "    df_ = df[df['Input12'] >= 1]\n",
    "    if step == 12:\n",
    "        input = df_['Input12'][:89856]\n",
    "        comple = df_['Comple12'][:89856]\n",
    "    elif step == 36:\n",
    "        input = df_['Input12'][:269568]\n",
    "        comple = df_['Comple12'][:269568]\n",
    "    mape = mean_absolute_percentage_error(input, comple)\n",
    "#     mape = format(mape, '.2%')\n",
    "    mae = mean_absolute_error(input, comple)\n",
    "    mse = mean_squared_error(input, comple)\n",
    "    rmse = mean_squared_error(input, comple, squared=False)\n",
    "    evs = explained_variance_score(input, comple)\n",
    "    r2 = r2_score(input, comple)\n",
    "    \n",
    "#     mape = mean_absolute_percentage_error(df_['Input'], df_['Predict'])\n",
    "# #     mape = format(mape, '.2%')\n",
    "#     mae = mean_absolute_error(df_['Input'], df_['Predict'])\n",
    "#     mse = mean_squared_error(df_['Input'], df_['Predict'])\n",
    "#     rmse = mean_squared_error(df_['Input'], df_['Predict'], squared=False)\n",
    "#     evs = explained_variance_score(df_['Input'], df_['Predict'])\n",
    "#     r2 = r2_score(df_['Input'], df_['Predict'])\n",
    "    \n",
    "    mae_FC_list.append(mae)\n",
    "    mse_FC_list.append(mse)\n",
    "    mape_FC_list.append(mape)\n",
    "    rmse_FC_list.append(rmse)\n",
    "    evs_FC_list.append(evs)\n",
    "    r2_FC_list.append(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FC Completion mae:  [10.874665696847842, 12.921235956197844]\n",
      "FC Completion mse:  [260.22717512683056, 375.9971037334225]\n",
      "FC Completion mape:  [0.2455877747539422, 0.2519903000025706]\n",
      "FC Completion rmse:  [16.13155836014706, 19.390644747749427]\n",
      "FC Completion evs:  [0.9688570665321024, 0.9550885992752293]\n",
      "FC Completion r2:  [0.9688289069538358, 0.9550850932738664]\n"
     ]
    }
   ],
   "source": [
    "print('FC Completion mae: ', mae_FC_list)\n",
    "print('FC Completion mse: ', mse_FC_list)\n",
    "print('FC Completion mape: ', mape_FC_list)\n",
    "print('FC Completion rmse: ', rmse_FC_list)\n",
    "print('FC Completion evs: ', evs_FC_list)\n",
    "print('FC Completion r2: ', r2_FC_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step = 12\n",
      "Step = 36\n"
     ]
    }
   ],
   "source": [
    "# transformer \n",
    "word_len = 1\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/'\n",
    "mae_transformer_list2 = []\n",
    "mse_transformer_list2 = []\n",
    "mape_transformer_list2 = []\n",
    "rmse_transformer_list2 = []\n",
    "evs_transformer_list2 = []\n",
    "r2_transformer_list2 = []\n",
    "# for step in [1,6,12,18,24,30,36,42,48,54,60,66,72]:\n",
    "for step in [12, 36]:\n",
    "    print(f'Step = {step}')\n",
    "    file = f'Transformer_Complement_{step}_0104.csv'\n",
    "    input_path = folder_path + file\n",
    "    df = pd.read_csv(input_path)\n",
    "    df_ = df[df['Input12'] >= 1]\n",
    "    \n",
    "    if step == 12:\n",
    "        input = df_['Input12'][:89856]\n",
    "        comple = df_['Comple12'][:89856]\n",
    "    elif step == 36:\n",
    "        input = df_['Input12'][:269568]\n",
    "        comple = df_['Comple12'][:269568]\n",
    "    mape = mean_absolute_percentage_error(input, comple)\n",
    "#     mape = format(mape, '.2%')\n",
    "    mae = mean_absolute_error(input, comple)\n",
    "    mse = mean_squared_error(input, comple)\n",
    "    rmse = mean_squared_error(input, comple, squared=False)\n",
    "    evs = explained_variance_score(input, comple)\n",
    "    r2 = r2_score(input, comple)\n",
    "    \n",
    "    mae_transformer_list2.append(mae)\n",
    "    mse_transformer_list2.append(mse)\n",
    "    mape_transformer_list2.append(mape)\n",
    "    rmse_transformer_list2.append(rmse)\n",
    "    evs_transformer_list2.append(evs)\n",
    "    r2_transformer_list2.append(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformer Completion mae:  [15.42461747519068, 19.329851300916776]\n",
      "Transformer Completion mse:  [395.1946640725579, 658.4086715542612]\n",
      "Transformer Completion mape:  [0.29112757230291003, 0.3420046716338106]\n",
      "Transformer Completion rmse:  [19.879503617358203, 25.65947527823321]\n",
      "Transformer Completion evs:  [0.9585966223650099, 0.949962345997143]\n",
      "Transformer Completion r2:  [0.9536599874856757, 0.9218328220926905]\n"
     ]
    }
   ],
   "source": [
    "print('Transformer Completion mae: ', mae_transformer_list2)\n",
    "print('Transformer Completion mse: ', mse_transformer_list2)\n",
    "print('Transformer Completion mape: ', mape_transformer_list2)\n",
    "print('Transformer Completion rmse: ', rmse_transformer_list2)\n",
    "print('Transformer Completion evs: ', evs_transformer_list2)\n",
    "print('Transformer Completion r2: ', r2_transformer_list2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ALL METRICS\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step = 1\n",
      "Step = 6\n",
      "Step = 12\n",
      "Step = 36\n",
      "Step = 72\n",
      "mae:  [2.3597004779216744, 4.262391046351662, 4.636128300546754, 7.658744799555328, 10.283719150575887]\n",
      "mape:  [0.08357187694016699, 0.15000445016625763, 0.14325344380058128, 0.18394442152616683, 0.22499934994248555]\n",
      "rmse:  [3.341017547061613, 5.735777370229291, 7.012598935596765, 12.478332194049662, 15.942556474179426]\n",
      "evs:  [0.9951834488729617, 0.9875765459899317, 0.9784172115229913, 0.9334345675335869, 0.8995985742390198]\n",
      "r2:  [0.9951754323443465, 0.9854588137140745, 0.9782055591384802, 0.9334170150725122, 0.8912918829640716]\n"
     ]
    }
   ],
   "source": [
    "word_len = 2\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_result_step36/'\n",
    "mae_list2 = []\n",
    "mse_list2 = []\n",
    "mape_list2 = []\n",
    "rmse_list2 = []\n",
    "evs_list2 = []\n",
    "r2_list2 = []\n",
    "for step in [1,6,12,36,72]:\n",
    "    print(f'Step = {step}')\n",
    "    file = f'{word_len}_{step}_0.1_step36.csv'\n",
    "    input_path = folder_path + file\n",
    "    df = pd.read_csv(input_path)\n",
    "#     df_ = df[df['Input'] >= 1]\n",
    "    \n",
    "    mape = mean_absolute_percentage_error(df['Input'], df['Predict'])\n",
    "#     mape = format(mape, '.2%')\n",
    "    mae = mean_absolute_error(df['Input'], df['Predict'])\n",
    "    mse = mean_squared_error(df['Input'], df['Predict'])\n",
    "    rmse = mean_squared_error(df['Input'], df['Predict'], squared=False)\n",
    "    evs = explained_variance_score(df['Input'], df['Predict'])\n",
    "    r2 = r2_score(df['Input'], df['Predict'])\n",
    "    \n",
    "    mae_list2.append(mae)\n",
    "    mse_list2.append(mse)\n",
    "    mape_list2.append(mape)\n",
    "    rmse_list2.append(rmse)\n",
    "    evs_list2.append(evs)\n",
    "    r2_list2.append(r2)\n",
    "    \n",
    "print('mae: ', mae_list2)\n",
    "# print('mse: ', mse_list2)\n",
    "print('mape: ', mape_list2)\n",
    "print('rmse: ', rmse_list2)\n",
    "print('evs: ', evs_list2)\n",
    "print('r2: ', r2_list2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step = 1\n",
      "Step = 6\n",
      "Step = 12\n",
      "Step = 36\n",
      "Step = 72\n",
      "mae:  [3.1763362752206534, 4.492800067353184, 3.896109429618501, 8.447626403419449, 9.8124845747549]\n",
      "mape:  [0.09607308142353757, 0.23146909611854039, 0.10044346794361762, 0.1919639314683357, 0.2591353343947632]\n",
      "rmse:  [4.17758819786893, 5.675921213388211, 5.9075588845433105, 15.589424361131952, 16.018993511156143]\n",
      "evs:  [0.9934459242044333, 0.9912541196000905, 0.9847676099336793, 0.8972751449323236, 0.8902473833436395]\n",
      "r2:  [0.9924568604397808, 0.9857607215511538, 0.9845330797163818, 0.8960773212061495, 0.8902469757655409]\n"
     ]
    }
   ],
   "source": [
    "word_len = 6\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_result_step36/'\n",
    "mae_list6 = []\n",
    "mse_list6 = []\n",
    "mape_list6 = []\n",
    "rmse_list6 = []\n",
    "evs_list6 = []\n",
    "r2_list6 = []\n",
    "for step in [1,6,12,36,72]:\n",
    "    print(f'Step = {step}')\n",
    "    file = f'{word_len}_{step}_0.1_step36.csv'\n",
    "    input_path = folder_path + file\n",
    "    df = pd.read_csv(input_path)\n",
    "#     df_ = df[df['Input'] >= 1]\n",
    "    \n",
    "    mape = mean_absolute_percentage_error(df['Input'], df['Predict'])\n",
    "#     mape = format(mape, '.2%')\n",
    "    mae = mean_absolute_error(df['Input'], df['Predict'])\n",
    "    mse = mean_squared_error(df['Input'], df['Predict'])\n",
    "    rmse = mean_squared_error(df['Input'], df['Predict'], squared=False)\n",
    "    evs = explained_variance_score(df['Input'], df['Predict'])\n",
    "    r2 = r2_score(df['Input'], df['Predict'])\n",
    "    \n",
    "    mae_list6.append(mae)\n",
    "    mse_list6.append(mse)\n",
    "    mape_list6.append(mape)\n",
    "    rmse_list6.append(rmse)\n",
    "    evs_list6.append(evs)\n",
    "    r2_list6.append(r2)\n",
    "    \n",
    "print('mae: ', mae_list6)\n",
    "# print('mse: ', mse_list6)\n",
    "print('mape: ', mape_list6)\n",
    "print('rmse: ', rmse_list6)\n",
    "print('evs: ', evs_list6)\n",
    "print('r2: ', r2_list6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step = 1\n",
      "Step = 6\n",
      "Step = 12\n",
      "Step = 36\n",
      "Step = 72\n",
      "mae:  [2.9932251926267033, 3.580704075426132, 4.649819896405585, 7.3299348834797575, 9.022114613741302]\n",
      "mape:  [0.11036868474988228, 0.09606282625645403, 0.13049934159516546, 0.16256123648401802, 0.20263311313137825]\n",
      "rmse:  [3.9030106004887806, 4.974281845100266, 6.591704203522633, 12.39643452913021, 13.718827662442397]\n",
      "evs:  [0.9938434867429844, 0.9891835676395889, 0.9820994912559213, 0.9348912397887101, 0.9216269015357546]\n",
      "r2:  [0.9934158404024621, 0.9890635582602585, 0.9807432430097772, 0.9342881405396782, 0.9195029349660957]\n"
     ]
    }
   ],
   "source": [
    "word_len = 24\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_result_step36/'\n",
    "mae_list24 = []\n",
    "mse_list24 = []\n",
    "mape_list24 = []\n",
    "rmse_list24 = []\n",
    "evs_list24 = []\n",
    "r2_list24 = []\n",
    "for step in [1,6,12,36,72]:\n",
    "    print(f'Step = {step}')\n",
    "    file = f'{word_len}_{step}_0.1_step36.csv'\n",
    "    input_path = folder_path + file\n",
    "    df = pd.read_csv(input_path)\n",
    "#     df_ = df[df['Input'] >= 1]\n",
    "    \n",
    "    mape = mean_absolute_percentage_error(df['Input'], df['Predict'])\n",
    "#     mape = format(mape, '.2%')\n",
    "    mae = mean_absolute_error(df['Input'], df['Predict'])\n",
    "    mse = mean_squared_error(df['Input'], df['Predict'])\n",
    "    rmse = mean_squared_error(df['Input'], df['Predict'], squared=False)\n",
    "    evs = explained_variance_score(df['Input'], df['Predict'])\n",
    "    r2 = r2_score(df['Input'], df['Predict'])\n",
    "    \n",
    "    mae_list24.append(mae)\n",
    "    mse_list24.append(mse)\n",
    "    mape_list24.append(mape)\n",
    "    rmse_list24.append(rmse)\n",
    "    evs_list24.append(evs)\n",
    "    r2_list24.append(r2)\n",
    "    \n",
    "print('mae: ', mae_list24)\n",
    "# print('mse: ', mse_list24)\n",
    "print('mape: ', mape_list24)\n",
    "print('rmse: ', rmse_list24)\n",
    "print('evs: ', evs_list24)\n",
    "print('r2: ', r2_list24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('/Users/gengyunxin/Documents/项目/traffic_model/giteee/finetune/test_result_all/complementation_result/Complement_Result1_12_-1.csv')\n",
    "df2 = pd.read_csv('/Users/gengyunxin/Documents/项目/traffic_model/giteee/finetune/test_result_all/complementation_result/Complement_Result2_12_-1.csv')\n",
    "df6 = pd.read_csv('/Users/gengyunxin/Documents/项目/traffic_model/giteee/finetune/test_result_all/complementation_result/Complement_Result6_12_-1.csv')\n",
    "df12 = pd.read_csv('/Users/gengyunxin/Documents/项目/traffic_model/giteee/finetune/test_result_all/complementation_result/Complement_Result12_12_-1.csv')\n",
    "df24 = pd.read_csv('/Users/gengyunxin/Documents/项目/traffic_model/giteee/finetune/test_result_all/complementation_result/Complement_Result24_12_-1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = np.array(df1['Input288'])\n",
    "c1 = np.array(df1['Complementation'])\n",
    "p1 = np.array(df1['Position'])\n",
    "c2 = np.array(df2['Complementation'])\n",
    "p2 = np.array(df2['Position'])\n",
    "c6 = np.array(df6['Complementation'])\n",
    "p6 = np.array(df6['Position'])\n",
    "c12 = np.array(df12['Complementation'])\n",
    "p12 = np.array(df12['Position'])\n",
    "c24 = np.array(df24['Complementation'])\n",
    "p24= np.array(df24['Position'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = pd.DataFrame({'Input': i[288*23:288*24],\n",
    "                       'Com1': c1[288*23:288*24],\n",
    "                       'Com2': c2[288*23:288*24],\n",
    "                       'Com6': c6[288*23:288*24],\n",
    "                       'Com12': c12[288*23:288*24],\n",
    "                       'Com24': c24[288*23:288*24],\n",
    "                       'Pos1': p1[288*23:288*24],\n",
    "                       'Pos2': p2[288*23:288*24],\n",
    "                       'Pos6': p6[288*23:288*24],\n",
    "                       'Pos12': p12[288*23:288*24],\n",
    "                       'Pos24': p24[288*23:288*24]})\n",
    "df_new.to_csv('zmy.csv', header=0, index=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbc696e4128>]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_new['Input'])\n",
    "plt.plot(df_new['Com24'][73:73+11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, pos in enumerate(p1):\n",
    "    if pos == p2[index] == p6[index] == p12[index] == p24[index] == 1.0:\n",
    "        print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6624"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "288*23\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6912"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "288*24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41.797382"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i[6624]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.674225"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i[6912]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_subplots(i,c1,p1,c2,p2,c6,p6,p12,c24,p24,index,seq=288):\n",
    "    for ind, pos in enumerate(p1):\n",
    "        if pos == 0.0:\n",
    "            c1[ind] = 0\n",
    "    for ind, pos in enumerate(p2):\n",
    "        if pos == 0.0:\n",
    "            c2[ind] = 0\n",
    "    for ind, pos in enumerate(p6):\n",
    "        if pos == 0.0:\n",
    "            c6[ind] = 0\n",
    "    for ind, pos in enumerate(p12):\n",
    "        if pos == 0.0:\n",
    "            c12[ind] = 0\n",
    "    for ind, pos in enumerate(p24):\n",
    "        if pos == 0.0:\n",
    "            c24[ind] = 0\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(321)\n",
    "    ax1.plot(i[seq*index:seq*(index+1)], label='Input')\n",
    "    ax1.plot(c1[seq*index:seq*(index+1)], label='Complement')\n",
    "#     ax1.legend(['Input', 'Complement'])\n",
    "    ax1.set_title('K=1')\n",
    "    \n",
    "    ax1 = fig.add_subplot(322)\n",
    "    ax1.plot(i[seq*index:seq*(index+1)], label='Input')\n",
    "    ax1.plot(c2[seq*index:seq*(index+1)], label='Complement')\n",
    "#     ax1.legend(['Input', 'Complement'])\n",
    "    ax1.set_title('K=2')\n",
    "    \n",
    "    ax1 = fig.add_subplot(323)\n",
    "    ax1.plot(i[seq*index:seq*(index+1)], label='Input')\n",
    "    ax1.plot(c6[seq*index:seq*(index+1)], label='Complement')\n",
    "#     ax1.legend(['Input', 'Complement'])\n",
    "    ax1.set_title('K=6')\n",
    "    \n",
    "    ax1 = fig.add_subplot(324)\n",
    "    ax1.plot(i[seq*index:seq*(index+1)], label='Input')\n",
    "    ax1.plot(c12[seq*index:seq*(index+1)], label='Complement')\n",
    "#     ax1.legend(['Input', 'Complement'])\n",
    "    ax1.set_title('K=12')\n",
    "    \n",
    "    ax1 = fig.add_subplot(313)\n",
    "    ax1.plot(i[seq*index:seq*(index+1)], label='Input')\n",
    "    ax1.plot(c24[seq*index:seq*(index+1)], label='Complement')\n",
    "#     ax1.legend(['Input', 'Complement'])\n",
    "    ax1.set_title('K=24')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:pytorch1.6.0] *",
   "language": "python",
   "name": "conda-env-pytorch1.6.0-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
